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": 1,
   "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": 2,
   "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": 3,
   "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-1.2.0 -- /opt/conda/bin/python3.7\n",
      "cachedir: .pytest_cache\n",
      "rootdir: /workspace/home\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 25.76s\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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32  # 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 = 0.004 # 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 = get_model_transfer_learning('resnet18', num_classes)\n",
    "# YOUR CODE HERE\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%|██████████████████████████████████| 40/40 [00:27<00:00,  1.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.052035\n",
      "\n",
      "\n",
      "Test Accuracy: 71% (895/1250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.052035373263061"
      ]
     },
     "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 [17:27<00:00,  1.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7104\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)"
   ]
  }
 ],
 "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
}