save_scripted_mobile_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from torch.utils.mobile_optimizer import optimize_for_mobile\n",
    "\n",
    "from core.recurrent_attention_model import RecurrentAttentionModel"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "initial_id",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "model_path = \"../_models/syllable_counter_model.pth\"\n",
    "model: RecurrentAttentionModel = torch.load(model_path, map_location='cpu')\n",
    "model.eval()"
   ],
   "id": "fd0439b03b03ffea",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Quantize\n",
    "model = torch.quantization.convert(model)"
   ],
   "id": "9736c74557d01987",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Trace model\n",
    "example_input = torch.randn(1, 128 * 3, 128)\n",
    "traced_model = torch.jit.trace(model, example_input)"
   ],
   "id": "29267c838fe646d1",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Optimize for mobile\n",
    "optimized_model = torch.utils.mobile_optimizer.optimize_for_mobile(traced_model)"
   ],
   "id": "57c1e0f23bcc9f3f",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "torch.jit.save(optimized_model, \"../_models/syllable_counter_model_mobile.pt\")",
   "id": "fb8f1be512627991",
   "outputs": [],
   "execution_count": null
  }
 ],
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