127 lines
4.0 KiB
Plaintext
127 lines
4.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "94ff7007",
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"metadata": {},
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"source": [
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"# Convert to ONNX\n",
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"\n",
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"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)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "311162fd-f957-4746-b524-25bb3e09efbc",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"from torch import nn\n",
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"import torch.utils.model_zoo as model_zoo\n",
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"import torch.onnx\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "fd182c6d-1e77-4bbb-bb53-8321d40ae002",
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"metadata": {},
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"outputs": [],
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"source": [
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"class TextCNN(nn.Module):\n",
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" def __init__(self, input_dim, num_classes):\n",
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" super(TextCNN, self).__init__()\n",
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" self.conv1 = nn.Conv1d(in_channels=input_dim, out_channels=DIMENSIONS, kernel_size=3, padding=1)\n",
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" self.conv2 = nn.Conv1d(in_channels=DIMENSIONS, out_channels=DIMENSIONS, kernel_size=4, padding=1)\n",
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" self.conv3 = nn.Conv1d(in_channels=DIMENSIONS, out_channels=DIMENSIONS, kernel_size=5, padding=2)\n",
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" \n",
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" self.bn1 = nn.BatchNorm1d(DIMENSIONS)\n",
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" self.bn2 = nn.BatchNorm1d(DIMENSIONS)\n",
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" self.bn3 = nn.BatchNorm1d(DIMENSIONS)\n",
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" \n",
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" self.dropout = nn.Dropout(0.5)\n",
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" self.fc = nn.Linear(DIMENSIONS * 3, num_classes)\n",
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"\n",
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" def forward(self, x):\n",
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" x = x.permute(0, 2, 1) # Change the input shape to (batch_size, embedding_dim, seq_length)\n",
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" \n",
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" x1 = F.relu(self.bn1(self.conv1(x)))\n",
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" x1 = F.adaptive_max_pool1d(x1, output_size=1).squeeze(2)\n",
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" \n",
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" x2 = F.relu(self.bn2(self.conv2(x)))\n",
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" x2 = F.adaptive_max_pool1d(x2, output_size=1).squeeze(2)\n",
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" \n",
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" x3 = F.relu(self.bn3(self.conv3(x)))\n",
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" x3 = F.adaptive_max_pool1d(x3, output_size=1).squeeze(2)\n",
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" \n",
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" x = torch.cat((x1, x2, x3), dim=1)\n",
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" x = self.dropout(x)\n",
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" x = self.fc(x)\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "bdb597cb-d896-485c-8c9c-897b1d35e8d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = torch.load(\"model.pt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9f7a6e64-75f2-4fa9-8d1e-b83099765d02",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# Example input: use random embedding vector to simulate real input\n",
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"dummy_input = torch.randn(1, 64, 128) # (batch_size, seq_length, embedding_dim)\n",
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"\n",
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"# Export model\n",
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"torch.onnx.export(\n",
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" model, # The model to export\n",
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" dummy_input, # Example input\n",
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" \"model.onnx\", # File name\n",
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" input_names=['input'], # Input name (Could customize)\n",
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" output_names=['output'], # Output name (Could customize)\n",
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" dynamic_axes={\n",
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" 'input': {0: 'batch_size', 1: 'seq_length'}, # Dynamic batch and sequence length\n",
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" 'output': {0: 'batch_size'}\n",
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" },\n",
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" opset_version=11 # ONNX version，ensure the ONNX runtime supports it\n",
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")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.19"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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