S11/EVA4S11.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "colab": {
      "name": "EVA4S11.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxWwB9Po6i5u",
        "colab_type": "text"
      },
      "source": [
        "# Installing Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JAM56MKzD2dq",
        "colab": {}
      },
      "source": [
        "!pip install --no-cache-dir -r requirements.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
      },
      "source": [
        "# Imports\n",
        "Importing necessary packages and modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yVpodAMpoCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "from tensornet.data import CIFAR10\n",
        "from tensornet.model import ResidualModel\n",
        "from tensornet.model.utils.loss import cross_entropy_loss\n",
        "from tensornet.model.utils.optimizers import sgd\n",
        "from tensornet.engine import LRFinder\n",
        "from tensornet.engine.ops.lr_scheduler import OneCycleLR\n",
        "from tensornet.utils import initialize_cuda, plot_metric\n",
        "from tensornet.utils.clr import CyclicLR"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ve_XDp27GSN_",
        "colab_type": "text"
      },
      "source": [
        "## Set Seed and Get GPU Availability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxVy79mmfQ1e",
        "colab_type": "code",
        "outputId": "d9f2b640-c652-4e13-83bd-c6572b138997",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Initialize CUDA and set random seed\n",
        "cuda, device = initialize_cuda(1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKi2t8X-CKJ5",
        "colab_type": "text"
      },
      "source": [
        "## Download Dataset\n",
        "\n",
        "Importing the CIFAR-10 class to download dataset and create data loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZjDtZVLGXh-",
        "colab_type": "code",
        "outputId": "193da377-30f4-4164-9b44-a8b5e33b5d45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "dataset = CIFAR10(\n",
        "    train_batch_size=512,\n",
        "    val_batch_size=512,\n",
        "    cuda=cuda,\n",
        "    num_workers=16,\n",
        "    padding=(40, 40),\n",
        "    crop=(32, 32),\n",
        "    horizontal_flip_prob=0.2,\n",
        "    cutout_prob=0.1,\n",
        "    cutout_dim=(8, 8),\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFcWRAeY_MsB",
        "colab_type": "text"
      },
      "source": [
        "## Training and Validation Dataloaders\n",
        "This is the final step in data preparation. It sets the dataloader arguments and then creates the dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8Tjigo2poCl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create train data loader\n",
        "train_loader = dataset.loader(train=True)\n",
        "\n",
        "# Create val data loader\n",
        "val_loader = dataset.loader(train=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSeDIqBq-6pC",
        "colab_type": "text"
      },
      "source": [
        "# Model Architecture and Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMmcgGBIguNc",
        "colab_type": "code",
        "outputId": "718fbee8-5012-40a1-bd96-cb5cd98aa56c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        }
      },
      "source": [
        "model = ResidualModel().to(device)  # Create model\n",
        "model.summary(dataset.image_size)  # Display model summary"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 64, 32, 32]           1,728\n",
            "       BatchNorm2d-2           [-1, 64, 32, 32]             128\n",
            "              ReLU-3           [-1, 64, 32, 32]               0\n",
            "            Conv2d-4          [-1, 128, 32, 32]          73,728\n",
            "         MaxPool2d-5          [-1, 128, 16, 16]               0\n",
            "       BatchNorm2d-6          [-1, 128, 16, 16]             256\n",
            "              ReLU-7          [-1, 128, 16, 16]               0\n",
            "            Conv2d-8          [-1, 128, 16, 16]         147,456\n",
            "       BatchNorm2d-9          [-1, 128, 16, 16]             256\n",
            "           Conv2d-10          [-1, 128, 16, 16]         147,456\n",
            "      BatchNorm2d-11          [-1, 128, 16, 16]             256\n",
            "       BasicBlock-12          [-1, 128, 16, 16]               0\n",
            "    ResidualBlock-13          [-1, 128, 16, 16]               0\n",
            "           Conv2d-14          [-1, 256, 16, 16]         294,912\n",
            "        MaxPool2d-15            [-1, 256, 8, 8]               0\n",
            "      BatchNorm2d-16            [-1, 256, 8, 8]             512\n",
            "             ReLU-17            [-1, 256, 8, 8]               0\n",
            "    ResidualBlock-18            [-1, 256, 8, 8]               0\n",
            "           Conv2d-19            [-1, 512, 8, 8]       1,179,648\n",
            "        MaxPool2d-20            [-1, 512, 4, 4]               0\n",
            "      BatchNorm2d-21            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-22            [-1, 512, 4, 4]               0\n",
            "           Conv2d-23            [-1, 512, 4, 4]       2,359,296\n",
            "      BatchNorm2d-24            [-1, 512, 4, 4]           1,024\n",
            "           Conv2d-25            [-1, 512, 4, 4]       2,359,296\n",
            "      BatchNorm2d-26            [-1, 512, 4, 4]           1,024\n",
            "       BasicBlock-27            [-1, 512, 4, 4]               0\n",
            "    ResidualBlock-28            [-1, 512, 4, 4]               0\n",
            "        MaxPool2d-29            [-1, 512, 1, 1]               0\n",
            "           Linear-30                   [-1, 10]           5,130\n",
            "================================================================\n",
            "Total params: 6,573,130\n",
            "Trainable params: 6,573,130\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 6.57\n",
            "Params size (MB): 25.07\n",
            "Estimated Total Size (MB): 31.65\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgYxmozxoZFq",
        "colab_type": "text"
      },
      "source": [
        "# LR Range Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_i8e0e2WB4f",
        "colab_type": "code",
        "outputId": "ebbadeaa-0845-4580-a241-9367c9ec2f34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "start_lr = 1e-6\n",
        "end_lr = 0.02\n",
        "num_iterations = 10 * len(train_loader)  # 10 epochs\n",
        "\n",
        "# Create optimizer and loss function\n",
        "optimizer = sgd(model, learning_rate=start_lr, momentum=0.9, l2_factor=0.01)\n",
        "criterion = cross_entropy_loss()\n",
        "\n",
        "# LR Range Test\n",
        "lr_finder = LRFinder(model, optimizer, criterion, metric='accuracy', device=device)\n",
        "lr_finder.range_test(\n",
        "    train_loader, num_iterations, mode='iteration',\n",
        "    end_lr=end_lr, step_mode='linear', diverge_th=0\n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iterations\n",
            "980/980 [========] - 240s 245ms/step - lr: 0.0101 - Accuracy: 56.3232\n",
            "Learning rate search finished.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSS6U_JZnmQg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "fca51960-a469-45ff-8f86-f2a6ccd65351"
      },
      "source": [
        "# Get best initial learning rate\n",
        "initial_lr = lr_finder.best_lr\n",
        "\n",
        "print('Learning Rate:', initial_lr)\n",
        "print('Accuracy:', lr_finder.best_metric)\n",
        "\n",
        "# Plot learning rate vs accuracy\n",
        "lr_finder.plot()\n",
        "\n",
        "# Reset graph\n",
        "lr_finder.reset()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning Rate: 0.02002040714285714\n",
            "Metric: 0.71405\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcyKrcQEGcdp",
        "colab_type": "text"
      },
      "source": [
        "# Model Training and Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk83iZb2uaEA",
        "colab_type": "code",
        "outputId": "f225dd50-c4ff-413b-a2b4-196707e52a55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 24\n",
        "\n",
        "# Define one cycle policy\n",
        "one_cycle_policy = OneCycleLR(\n",
        "    optimizer, initial_lr, epochs=epochs,\n",
        "    steps_per_epoch=len(train_loader), pct_start=5/epochs,\n",
        "    div_factor=10, final_div_factor=1\n",
        ")\n",
        "\n",
        "# Train model\n",
        "model.fit(\n",
        "    train_loader, optimizer, criterion, device=device, epochs=epochs,\n",
        "    val_loader=val_loader, callbacks=[one_cycle_policy]\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1:\n",
            "98/98 [========] - 24s 246ms/step - loss: 1.8651 - accuracy: 30.6704\n",
            "Validation set: Average loss: 0.0024, Accuracy: 57.36%\n",
            "\n",
            "Epoch 2:\n",
            "98/98 [========] - 24s 247ms/step - loss: 1.1717 - accuracy: 46.1496\n",
            "Validation set: Average loss: 0.0019, Accuracy: 66.88%\n",
            "\n",
            "Epoch 3:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.9898 - accuracy: 52.9654\n",
            "Validation set: Average loss: 0.0023, Accuracy: 64.89%\n",
            "\n",
            "Epoch 4:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.7610 - accuracy: 58.0047\n",
            "Validation set: Average loss: 0.0016, Accuracy: 73.38%\n",
            "\n",
            "Epoch 5:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.6404 - accuracy: 62.1273\n",
            "Validation set: Average loss: 0.0012, Accuracy: 78.92%\n",
            "\n",
            "Epoch 6:\n",
            "98/98 [========] - 24s 250ms/step - loss: 0.4953 - accuracy: 65.5024\n",
            "Validation set: Average loss: 0.0013, Accuracy: 79.57%\n",
            "\n",
            "Epoch 7:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.4355 - accuracy: 68.3767\n",
            "Validation set: Average loss: 0.0010, Accuracy: 83.09%\n",
            "\n",
            "Epoch 8:\n",
            "98/98 [========] - 24s 245ms/step - loss: 0.3859 - accuracy: 70.7052\n",
            "Validation set: Average loss: 0.0011, Accuracy: 81.82%\n",
            "\n",
            "Epoch 9:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.3422 - accuracy: 72.6984\n",
            "Validation set: Average loss: 0.0011, Accuracy: 81.80%\n",
            "\n",
            "Epoch 10:\n",
            "98/98 [========] - 24s 246ms/step - loss: 0.3196 - accuracy: 74.3906\n",
            "Validation set: Average loss: 0.0011, Accuracy: 81.74%\n",
            "\n",
            "Epoch 11:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.2914 - accuracy: 75.8474\n",
            "Validation set: Average loss: 0.0010, Accuracy: 82.52%\n",
            "\n",
            "Epoch 12:\n",
            "98/98 [========] - 24s 246ms/step - loss: 0.2760 - accuracy: 77.1183\n",
            "Validation set: Average loss: 0.0010, Accuracy: 83.24%\n",
            "\n",
            "Epoch 13:\n",
            "98/98 [========] - 24s 245ms/step - loss: 0.2507 - accuracy: 78.2450\n",
            "Validation set: Average loss: 0.0008, Accuracy: 86.48%\n",
            "\n",
            "Epoch 14:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.2324 - accuracy: 79.2799\n",
            "Validation set: Average loss: 0.0008, Accuracy: 86.83%\n",
            "\n",
            "Epoch 15:\n",
            "98/98 [========] - 24s 246ms/step - loss: 0.2124 - accuracy: 80.2184\n",
            "Validation set: Average loss: 0.0008, Accuracy: 87.60%\n",
            "\n",
            "Epoch 16:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.1967 - accuracy: 81.0889\n",
            "Validation set: Average loss: 0.0007, Accuracy: 88.35%\n",
            "\n",
            "Epoch 17:\n",
            "98/98 [========] - 24s 246ms/step - loss: 0.1856 - accuracy: 81.8753\n",
            "Validation set: Average loss: 0.0007, Accuracy: 87.50%\n",
            "\n",
            "Epoch 18:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.1677 - accuracy: 82.6132\n",
            "Validation set: Average loss: 0.0008, Accuracy: 86.55%\n",
            "\n",
            "Epoch 19:\n",
            "98/98 [========] - 24s 248ms/step - loss: 0.1451 - accuracy: 83.3044\n",
            "Validation set: Average loss: 0.0008, Accuracy: 87.52%\n",
            "\n",
            "Epoch 20:\n",
            "98/98 [========] - 24s 244ms/step - loss: 0.1334 - accuracy: 83.9632\n",
            "Validation set: Average loss: 0.0006, Accuracy: 90.52%\n",
            "\n",
            "Epoch 21:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.1123 - accuracy: 84.5866\n",
            "Validation set: Average loss: 0.0006, Accuracy: 90.87%\n",
            "\n",
            "Epoch 22:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.0940 - accuracy: 85.1871\n",
            "Validation set: Average loss: 0.0005, Accuracy: 91.97%\n",
            "\n",
            "Epoch 23:\n",
            "98/98 [========] - 24s 247ms/step - loss: 0.0878 - accuracy: 85.7490\n",
            "Validation set: Average loss: 0.0005, Accuracy: 92.26%\n",
            "\n",
            "Epoch 24:\n",
            "98/98 [========] - 24s 246ms/step - loss: 0.0799 - accuracy: 86.2732\n",
            "Validation set: Average loss: 0.0005, Accuracy: 90.90%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cV5et8bm91J5",
        "colab_type": "text"
      },
      "source": [
        "# Cyclic LR Triangular Schedule"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hNpEDD_92Mr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "31052f22-900c-4e5b-bd29-52d42be9321c"
      },
      "source": [
        "triangular_lr = CyclicLR(initial_lr, initial_lr / 10, len(train_loader), len(train_loader) * 10)\n",
        "triangular_lr.cyclic_lr(plot=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o99Jq1XTdo-",
        "colab_type": "text"
      },
      "source": [
        "## Plotting Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMFYHCFVA7Z-",
        "colab_type": "code",
        "outputId": "9a5f8d9a-abfb-486f-fdac-53d20343f608",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "plot_metric({\n",
        "    'Training': model.learner.train_accuracies,\n",
        "    'Validation': model.learner.val_accuracies\n",
        "}, 'Accuracy')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBXFeKGvXJba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}