transfer_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks\n",
    "\n",
    "## Project: Write an Algorithm for Landmark Classification\n",
    "\n",
    "\n",
    "### Transfer learning\n",
    "\n",
    "In the previous notebook we have trained our own CNN and we got a certain performance. Let's see how hard it is to match that performance with transfer learning.\n",
    "\n",
    "---\n",
    "## <img src=\"static_images/icons/noun-advance-2109145.png\" alt=\">\" style=\"width:50px\"/> Step 0: Setting up\n",
    "\n",
    "The following cells make sure that your environment is setup correctly and check that your GPU is available and ready to go. You have to execute them every time you restart your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\r\n"
     ]
    }
   ],
   "source": [
    "# Install requirements\n",
    "!pip install -r requirements.txt | grep -v \"already satisfied\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available\n",
      "Dataset already downloaded. If you need to re-download, please delete the directory /data/DLND/C2/landmark_images\n",
      "Reusing cached mean and std\n"
     ]
    }
   ],
   "source": [
    "from src.helpers import setup_env\n",
    "\n",
    "# If running locally, this will download dataset (make sure you have at \n",
    "# least 2 Gb of space on your hard drive)\n",
    "setup_env()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <img src=\"static_images/icons/noun-advance-2109145.png\" alt=\">\" style=\"width:50px\"/> Step 1: Create transfer learning architecture\n",
    "\n",
    "Open the file `src/transfer.py` and complete the `get_model_transfer_learning` function. When you are done, execute this test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m============================= test session starts ==============================\u001b[0m\n",
      "platform linux -- Python 3.7.6, pytest-7.1.1, pluggy-1.0.0 -- /opt/conda/bin/python3.7\n",
      "cachedir: .pytest_cache\n",
      "rootdir: /home/workspace\n",
      "collected 1 item                                                               \u001b[0m\u001b[1m\n",
      "\n",
      "src/transfer.py::test_get_model_transfer_learning \u001b[32mPASSED\u001b[0m\u001b[32m                 [100%]\u001b[0m\n",
      "\n",
      "\u001b[32m============================== \u001b[32m\u001b[1m1 passed\u001b[0m\u001b[32m in 6.34s\u001b[0m\u001b[32m ===============================\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pytest -vv src/transfer.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <img src=\"static_images/icons/noun-advance-2109145.png\" alt=\">\" style=\"width:50px\"/> Step 2: Train, validation and test\n",
    "\n",
    "Let's train our transfer learning model! Let's start defining the hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64  # size of the minibatch for stochastic gradient descent (or Adam)\n",
    "valid_size = 0.2  # fraction of the training data to reserve for validation\n",
    "num_epochs = 50  # number of epochs for training\n",
    "num_classes = 50  # number of classes. Do not change this\n",
    "learning_rate = 0.001  # Learning rate for SGD (or Adam)\n",
    "opt = 'adam'      # optimizer. 'sgd' or 'adam'\n",
    "weight_decay = 0.0 # regularization. Increase this to combat overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.data import get_data_loaders\n",
    "from src.optimization import get_optimizer, get_loss\n",
    "from src.train import optimize\n",
    "from src.transfer import get_model_transfer_learning\n",
    "\n",
    "# Get a model using get_model_transfer_learning. Use one of the names reported here:\n",
    "# https://pytorch.org/vision/0.10/models.html\n",
    "# For example, if you want to load ResNet 18, use \"resnet18\"\n",
    "# NOTE: use the hyperparameters defined in the previous cell, do NOT copy/paste the\n",
    "# values\n",
    "#model_transfer = # YOUR CODE HERE\n",
    "model_transfer = get_model_transfer_learning(model_name=\"resnet18\", n_classes=50)\n",
    "\n",
    "# train the model\n",
    "data_loaders = get_data_loaders(batch_size=batch_size)\n",
    "optimizer = get_optimizer(\n",
    "    model_transfer,\n",
    "    learning_rate=learning_rate,\n",
    "    optimizer=opt,\n",
    "    weight_decay=weight_decay,\n",
    ")\n",
    "loss = get_loss()\n",
    "\n",
    "optimize(\n",
    "    data_loaders,\n",
    "    model_transfer,\n",
    "    optimizer,\n",
    "    loss,\n",
    "    n_epochs=num_epochs,\n",
    "    save_path=\"checkpoints/model_transfer.pt\",\n",
    "    interactive_tracking=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static_images/icons/noun-question-mark-869751.png\" alt=\"?\" style=\"width:25px\"/> __Question:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static_images/icons/noun-answer-3361020.png\" alt=\">\" style=\"width:25px\"/>  __Answer:__ I decided to use ResNet18 for the base of my model, since it performs fairly well on ImageNet and is not too large of a model. Also, since ResNet18 was trained for the ImageNet task, it is a good model to use for this landmark classificaiton task, since both ImageNet and this landmark task use images of natural scenes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now play with the hyperparameters and see which performance you can get on the validation set. You should get at least 60% for a passing grade, but a good model choice and a good training strategy could get you up to 80% or so. Let's see how close you can get!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <img src=\"static_images/icons/noun-advance-2109145.png\" alt=\">\" style=\"width:50px\"/> Step 3: Test the Model\n",
    "\n",
    "Try out your model on the test dataset of landmark images. Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 60% and matches more or less what you got on the validation set (otherwise you're overfitting!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████████████████████████████| 20/20 [00:10<00:00,  1.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.929448\n",
      "\n",
      "\n",
      "Test Accuracy: 75% (949/1250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9294477537274359"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from src.train import one_epoch_test\n",
    "from src.transfer import get_model_transfer_learning\n",
    "\n",
    "model_transfer = get_model_transfer_learning(\"resnet18\", n_classes=num_classes)\n",
    "# Load saved weights\n",
    "model_transfer.load_state_dict(torch.load('checkpoints/model_transfer.pt'))\n",
    "\n",
    "one_epoch_test(data_loaders['test'], model_transfer, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <img src=\"static_images/icons/noun-advance-2109145.png\" alt=\">\" style=\"width:50px\"/> Step 4: Export using torchscript\n",
    "\n",
    "Now, just like we did with our original model, we export the best fit model using torchscript so that it can be used in our application:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing cached mean and std\n"
     ]
    }
   ],
   "source": [
    "from src.predictor import Predictor\n",
    "from src.helpers import compute_mean_and_std\n",
    "\n",
    "# First let's get the class names from our data loaders\n",
    "class_names = data_loaders[\"train\"].dataset.classes\n",
    "\n",
    "# Then let's move the model_transfer to the CPU\n",
    "# (we don't need GPU for inference)\n",
    "model_transfer = model_transfer.cpu()\n",
    "# Let's make sure we use the right weights by loading the\n",
    "# best weights we have found during training\n",
    "# NOTE: remember to use map_location='cpu' so the weights\n",
    "# are loaded on the CPU (and not the GPU)\n",
    "model_transfer.load_state_dict(\n",
    "    torch.load(\"checkpoints/model_transfer.pt\", map_location=\"cpu\")\n",
    ")\n",
    "\n",
    "# Let's wrap our model using the predictor class\n",
    "mean, std = compute_mean_and_std()\n",
    "predictor = Predictor(model_transfer, class_names, mean, std).cpu()\n",
    "\n",
    "# Export using torch.jit.script\n",
    "scripted_predictor = torch.jit.script(predictor)\n",
    "scripted_predictor.save(\"checkpoints/transfer_exported.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 1250/1250 [01:43<00:00, 12.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7504\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from src.predictor import predictor_test\n",
    "from src.helpers import plot_confusion_matrix\n",
    "\n",
    "model_reloaded = torch.jit.load(\"checkpoints/transfer_exported.pt\")\n",
    "\n",
    "pred, truth = predictor_test(data_loaders['test'], model_reloaded)\n",
    "\n",
    "plot_confusion_matrix(pred, truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}