integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k39CzQqsOeQE"
   },
   "source": [
    "# **Image Classification: Sparse Transfer Learning with the Python API**\n",
    "\n",
    "In this example, you will fine-tune a 95% pruned-quantized ResNet-50 model onto the Imagenette dataset using SparseML's Torchvision Integration.\n",
    "\n",
    "### **Sparse Transfer Learning Overview**\n",
    "\n",
    "Sparse Transfer Learning is very similiar to typical fine-tuning you are used to when training models. However, with Sparse Transfer Learning, we start the training process from a pre-sparsified checkpoint and maintain the sparsity structure while the fine tuning occurs. \n",
    "\n",
    "At the end, you will have a sparse model trained on your dataset, ready to be deployed with DeepSparse for GPU-class performance on CPUs!\n",
    "\n",
    "### **Pre-Sparsified Image Classification Model**\n",
    "SparseZoo, Neural Magic's open source repository of pre-sparsified models, contains several families of image classification models including ResNet, MobileNet, and EfficientNet.\n",
    "\n",
    "In this case, we start the training process from a 95% pruned-quantized [ResNet-50 model](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none), which is identified by the following SparseZoo stub:\n",
    "\n",
    "```bash\n",
    "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none\n",
    "```\n",
    "\n",
    "\n",
    "**Let's dive in!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0WybTbssU0g"
   },
   "source": [
    "## **Installation**\n",
    "\n",
    "Install SparseML via `pip`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OCGA94en19CF"
   },
   "outputs": [],
   "source": [
    "!pip install sparseml[torch,torchvision]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OWXoz-YnPR8Z"
   },
   "source": [
    "If you are running on Google Colab, restart the runtime after this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "H5DZiJrH3DiO"
   },
   "outputs": [],
   "source": [
    "import sparseml\n",
    "import sparsezoo\n",
    "import torch\n",
    "import torchvision\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "th1dNZiY-U56"
   },
   "source": [
    "## **Step 1: Download the Sparse Checkpoint from the SparseZoo**\n",
    "\n",
    "We can use a SparseZoo stub to identify the model we want to use. In this case, we will use the 95% pruned and quantized version, which retains 99% of the baseline accuracy of the dense model. \n",
    "\n",
    "[Checkout the model card on SparseZoo](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Wk_EC5ar3F0L"
   },
   "outputs": [],
   "source": [
    "from sparseml.pytorch.models import ModelRegistry\n",
    "\n",
    "ZOO_STUB = \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification\"\n",
    "\n",
    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
    "model = ModelRegistry.create(\n",
    "    key=\"resnet50\",\n",
    "    pretrained_path=ZOO_STUB,\n",
    "    pretrained_dataset=\"imagenette\",\n",
    "    num_classes=10,\n",
    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
    ")\n",
    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
    "input_size = input_shape[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oMIVY3hYAgI8"
   },
   "source": [
    "## **Step 2: Setup The Dataset**\n",
    "\n",
    "Download the datasets. We will use Imagenette dataset for this example. Checkout the [PyTorch documentation](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) on Datasets and Dataloaders for more derails on how Datasets work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KZBaOzAe-fQj"
   },
   "outputs": [],
   "source": [
    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
    "\n",
    "# Define your train and validation datasets below, here we will use Imagenette\n",
    "print(\"\\nloading train dataset...\")\n",
    "train_dataset = ImagenetteDataset(\n",
    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
    ")\n",
    "\n",
    "print(\"\\nloading val dataset...\")\n",
    "val_dataset = ImagenetteDataset(\n",
    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AA-J71G1_c_H"
   },
   "source": [
    "## **Step 3: Setup Training Loop and Objects**\n",
    "\n",
    "This training loop is all native PyTorch, there is nothing SparseML related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QX9y0eBe4Bu4"
   },
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "import math\n",
    "\n",
    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
    "    if train:\n",
    "        model.train()\n",
    "    else:\n",
    "        model.eval()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    total_correct = 0\n",
    "    total_predictions = 0\n",
    "\n",
    "    # loop through batches\n",
    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        if train:\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "        # compute loss, run backpropogation\n",
    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
    "        loss = criterion(outputs, labels)\n",
    "        if train:\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "\n",
    "        # run evaluation\n",
    "        predictions = outputs.argmax(dim=1)\n",
    "        total_correct += torch.sum(predictions == labels).item()\n",
    "        total_predictions += inputs.size(0)\n",
    "\n",
    "    # return loss and evaluation metric\n",
    "    loss = running_loss / (step + 1.0)\n",
    "    accuracy = total_correct / total_predictions\n",
    "    return loss, accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GQL_dmO7QHQO"
   },
   "source": [
    "Setup the dataloaders, loss, and optimizer. These are all native PyTorch, there is nothing SparseML related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "In57pouf4P6r"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.optim import Adam\n",
    "\n",
    "# hyperparameters\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "# setup device\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model.to(device)\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# setup data loaders\n",
    "train_loader = DataLoader(\n",
    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
    ")\n",
    "val_loader = DataLoader(\n",
    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
    ")\n",
    "\n",
    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
    "criterion = CrossEntropyLoss()\n",
    "optimizer = Adam(model.parameters(), lr=8e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dUx2vnrrAwdX"
   },
   "source": [
    "## **Step 5: Add Sparse Transfer Learning Logic with SparseML**\n",
    "\n",
    "First, we will download the use the SparseZoo `Model` class to download the transfer learning recipe to the local directory. You can also download directly from the SparseZoo website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PT_04J827o_a"
   },
   "outputs": [],
   "source": [
    "from sparsezoo import Model\n",
    "\n",
    "download_directory = \"./model_download_directory\"\n",
    "zoo_model = Model(ZOO_STUB, download_path = download_directory)\n",
    "recipe_path = zoo_model.recipes.default.path\n",
    "print(recipe_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KCMRFLFN8024"
   },
   "source": [
    "#### **Inspecting the Recipe**\n",
    "\n",
    "Here is the transfer learning recipe:\n",
    "\n",
    "```yaml\n",
    "# Epoch and Learning-Rate variables\n",
    "num_epochs: 10.0\n",
    "init_lr: 0.0005\n",
    "\n",
    "# quantization variables\n",
    "quantization_epochs: 6.0\n",
    "\n",
    "training_modifiers:\n",
    "  - !EpochRangeModifier\n",
    "    start_epoch: 0.0\n",
    "    end_epoch: eval(num_epochs)\n",
    "\n",
    "  - !LearningRateFunctionModifier\n",
    "    final_lr: 0.0\n",
    "    init_lr: eval(init_lr)\n",
    "    lr_func: cosine\n",
    "    start_epoch: 0.0\n",
    "    end_epoch: eval(num_epochs)\n",
    "\n",
    "# Phase 1 Sparse Transfer Learning / Recovery\n",
    "sparse_transfer_learning_modifiers:\n",
    "  - !ConstantPruningModifier\n",
    "    start_epoch: 0.0\n",
    "    params: __ALL_PRUNABLE__\n",
    "\n",
    "# Phase 2 Apply quantization\n",
    "sparse_quantized_transfer_learning_modifiers:\n",
    "  - !QuantizationModifier\n",
    "    start_epoch: eval(num_epochs - quantization_epochs)\n",
    "```\n",
    "\n",
    "\n",
    "The `Modifiers` in the transfer learning recipe are the important items that encode how SparseML should modify the training process for Sparse Transfer Learning:\n",
    "- `ConstantPruningModifier` tells SparseML to pin weights at 0 over all epochs, maintaining the sparsity structure of the network\n",
    "- `QuantizationModifier` tells SparseML to quanitze the weights with quantization aware training over the last 5 epochs\n",
    "\n",
    "\n",
    "To transfer learn this sparsified model to other datasets you may have to adjust certain hyperparameters in this recipe and/or training script. Some considerations:\n",
    "\n",
    "- For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly\n",
    "- Adding more learning rate step milestones can lead to more jumps in accuracy\n",
    "- Increase the learning rate when increasing batch size\n",
    "- Increase the number of epochs if using SGD instead of the Adam optimizer\n",
    "- Update the base learning rate based on the number of steps needed to train your dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l5XhMbqb_7FM"
   },
   "source": [
    "Next, we will use SparseML's `ScheduledModifierManager` to parse the instructions from the recipes and update the `optimizer` and `model` with the instructions encoded therein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xvbZ-avB4xdc"
   },
   "outputs": [],
   "source": [
    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
    "\n",
    "# create ScheduledModifierManager and Optimizer wrapper\n",
    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nrgF_ur_B66y"
   },
   "source": [
    "## **Step 6: Run Training**\n",
    "\n",
    "We pass the modified `model` and `optimizer` objects to the training loop we build above. Since we already called `manager.modify` all of the logic from the recipe is encoded in the `optimizer` and `model`. We can use the `model` and `optimizer`, and the SparseML system applies the algorithms specified in the recipe. In this case, since we applied a `ConstantPruningModifier`, the weights which are zero will be pinned to 0 during the fine-tuning, maintaining the sparsity structure as the model fine-tunes."
   ]
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    },
    "executionInfo": {
     "elapsed": 1988739,
     "status": "ok",
     "timestamp": 1674842564679,
     "user": {
      "displayName": "Robert Shaw",
      "userId": "07936720368498181527"
     },
     "user_tz": 300
    },
    "id": "xy7JLoTM6RC_",
    "outputId": "7f956859-081b-49fd-9723-e93a55e74c72"
   },
   "outputs": [],
   "source": [
    "# run transfer learning\n",
    "epoch = manager.min_epochs\n",
    "for epoch in range(manager.max_epochs):\n",
    "    # run training loop\n",
    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
    "    print(f\"Running Training Epoch {epoch_name}\")\n",
    "    train_loss, train_acc = run_model_one_epoch(\n",
    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
    "    )\n",
    "    print(\n",
    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
    "    )\n",
    "\n",
    "    # run validation loop\n",
    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
    "    print(\n",
    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
    "    )\n",
    "\n",
    "manager.finalize(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZO9JUL6FD8kq"
   },
   "source": [
    "We can see that the model is sparse. This command prints out the layerwise sparsity structure of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1674842564680,
     "user": {
      "displayName": "Robert Shaw",
      "userId": "07936720368498181527"
     },
     "user_tz": 300
    },
    "id": "1cHaXmcu4zGM",
    "outputId": "426d92df-0aeb-4d61-8774-5f7ede0029b7"
   },
   "outputs": [],
   "source": [
    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
    "\n",
    "# print sparsities of each layer\n",
    "for (name, layer) in get_prunable_layers(model):\n",
    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WOSrElENDKcm"
   },
   "source": [
    "## **Step 7: Export To ONNX**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OJRVB-ZSDJ-6"
   },
   "outputs": [],
   "source": [
    "from sparseml.pytorch.utils import ModuleExporter\n",
    "\n",
    "save_dir = \"pytorch_classification\"\n",
    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"model.onnx\", convert_qat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ffudiHTYDRC4"
   },
   "source": [
    "## **Step 8: Run Inference with DeepSparse**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5jqkTY3vFyG9"
   },
   "outputs": [],
   "source": [
    "!pip3 install deepsparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AdUKQ-FNF66P"
   },
   "outputs": [],
   "source": [
    "from deepsparse import Pipeline\n",
    "ic_pipeline = Pipeline.create(\n",
    "    \"image_classification\",\n",
    "    model_path=\"/content/pytorch_classification/model.onnx\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l38TLFevKfpf"
   },
   "source": [
    "Class 6 is `garbage_truck`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 441,
     "status": "ok",
     "timestamp": 1674846828912,
     "user": {
      "displayName": "Robert Shaw",
      "userId": "07936720368498181527"
     },
     "user_tz": 300
    },
    "id": "eJv4S9fkGjdO",
    "outputId": "df458b06-3ff4-4a0d-c1c2-3689799abaf5"
   },
   "outputs": [],
   "source": [
    "img_path = \"/root/.cache/nm_datasets/imagenette/imagenette-320/val/n03417042/ILSVRC2012_val_00001144.JPEG\" \n",
    "images = [img_path]\n",
    "print(ic_pipeline(images=images).labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1674846829838,
     "user": {
      "displayName": "Robert Shaw",
      "userId": "07936720368498181527"
     },
     "user_tz": 300
    },
    "id": "79Xj9eqvJXaD",
    "outputId": "2bdfbe4d-ab3c-4cc4-c54f-a6be6cbb2b2b"
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = np.array(Image.open(img_path))\n",
    "plt.matshow(img)"
   ]
  }
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