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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collect metrics using a pre-trained model\n",
    "\n",
    "This notebook demonstrates how to use a pre-trained model to collect metrics on a dataset.\n",
    "\n",
    "![](../images/cifar10-collect-metrics.png)\n",
    "\n",
    "<!-- Tags: [\"CIFAR-10\", \"collect-metrics\"] -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install 3lc\n",
    "%pip install timm\n",
    "%pip install git+https://github.com/3lc-ai/3lc-examples.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timm\n",
    "import tlc\n",
    "import torchvision\n",
    "\n",
    "from tlc_tools.common import infer_torch_device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "NUM_WORKERS = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Tables\n",
    "\n",
    "We will reuse the tables from the notebook [create-table-from-torch.ipynb](../1-create-tables/create-table-from-torch-dataset.ipynb),\n",
    "and use a pre-trained model from Hugging Face Hub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = infer_torch_device()\n",
    "\n",
    "# Use a resnet18 model from timm, already trained on CIFAR-10\n",
    "model = timm.create_model(\"hf_hub:FredMell/resnet18-cifar10\", pretrained=True).to(device)\n",
    "\n",
    "# Load the tables\n",
    "train_table = tlc.Table.from_names(\"initial\", \"CIFAR-10-train\", \"3LC Tutorials - CIFAR-10\")\n",
    "val_table = tlc.Table.from_names(\"initial\", \"CIFAR-10-val\", \"3LC Tutorials - CIFAR-10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_transform = torchvision.transforms.Compose(\n",
    "    [\n",
    "        torchvision.transforms.Resize(224),\n",
    "        torchvision.transforms.ToTensor(),\n",
    "        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "def transform(sample):\n",
    "    image = sample[0]\n",
    "    label = sample[1]\n",
    "    return (image_transform(image), label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the transforms to the tables to ensure model-compatibility (ensure any existing maps are cleared first)\n",
    "\n",
    "train_table.clear_maps()\n",
    "train_table = train_table.map(transform)\n",
    "\n",
    "val_table.clear_maps()\n",
    "val_table = val_table.map(transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 3LC run and collect metrics\n",
    "run = tlc.init(\n",
    "    project_name=train_table.project_name,\n",
    "    description=\"Only collect metrics with trained model on CIFAR-10\",\n",
    ")\n",
    "\n",
    "dataloader_args = {\n",
    "    \"batch_size\": 128,\n",
    "    \"num_workers\": NUM_WORKERS,\n",
    "    \"pin_memory\": True,\n",
    "}\n",
    "\n",
    "classes = list(train_table.get_simple_value_map(\"Label\").values())\n",
    "\n",
    "tlc.collect_metrics(\n",
    "    table=train_table,\n",
    "    predictor=model,\n",
    "    metrics_collectors=tlc.ClassificationMetricsCollector(classes=classes),\n",
    "    dataloader_args=dataloader_args,\n",
    "    split=\"train\",\n",
    ")\n",
    "\n",
    "tlc.collect_metrics(\n",
    "    table=val_table,\n",
    "    predictor=model,\n",
    "    metrics_collectors=tlc.ClassificationMetricsCollector(classes=classes),\n",
    "    dataloader_args=dataloader_args,\n",
    "    split=\"val\",\n",
    ")\n",
    "\n",
    "run.set_status_completed()"
   ]
  }
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