pytorch_basics.ipynb

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            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
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            "grid_auto_rows": null,
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            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
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            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywzwXi4qTmw8",
        "outputId": "bff58e1c-7dd5-442b-e4bf-1bca1fff3764"
      },
      "source": [
        "!pip install torchvision.transformer"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement torchvision.transformer (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for torchvision.transformer\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQasnzi5SHZb",
        "outputId": "a77469da-799f-4c14-ad01-199f982081f3"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# basic autograd \n",
        "x = torch.tensor(1., requires_grad=True)\n",
        "w = torch.tensor(2., requires_grad=True)\n",
        "b = torch.tensor(3., requires_grad=True)\n",
        "\n",
        "# build a computational graph\n",
        "y = w * x + b # y = 2 * x + 3\n",
        "\n",
        "# compute gradients\n",
        "y.backward()\n",
        "\n",
        "# print out the gradients\n",
        "print(x.grad)\n",
        "print(w.grad)\n",
        "print(b.grad)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(2.)\n",
            "tensor(1.)\n",
            "tensor(1.)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4ntw_BJUHlA",
        "outputId": "89c35c55-c559-4b61-a050-9cfcff864a74"
      },
      "source": [
        "# create tensors of shape (10, 3) and (10, 2)\n",
        "x = torch.randn(10, 3)\n",
        "y = torch.randn(10, 2)\n",
        "\n",
        "# build a fully connected layer\n",
        "linear = nn.Linear(3, 2)\n",
        "print('w: ', linear.weight)\n",
        "print('b: ', linear.bias)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w:  Parameter containing:\n",
            "tensor([[ 0.1838,  0.1479,  0.5650],\n",
            "        [-0.0821, -0.4800,  0.2258]], requires_grad=True)\n",
            "b:  Parameter containing:\n",
            "tensor([ 0.5554, -0.2077], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XL-Uq8diU3Cv",
        "outputId": "032e05b8-bd34-4884-ca47-154c6af29018"
      },
      "source": [
        "# Build loss function and optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)\n",
        "print(criterion, optimizer)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSELoss() SGD (\n",
            "Parameter Group 0\n",
            "    dampening: 0\n",
            "    lr: 0.01\n",
            "    momentum: 0\n",
            "    nesterov: False\n",
            "    weight_decay: 0\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhBn8YF_VUmn",
        "outputId": "c450b8a3-a4f7-4f7d-eb1d-ebdd37576568"
      },
      "source": [
        "# Forward pass\n",
        "pred = linear(x)\n",
        "\n",
        "# Compute loss.\n",
        "loss = criterion(pred, y)\n",
        "print('loss:', loss.item())\n",
        "\n",
        "# Backward pass.\n",
        "loss.backward()\n",
        "\n",
        "# Print out the gradients.\n",
        "print('dL/dw: ', linear.weight.grad)\n",
        "print('dL/db: ', linear.bias.grad)\n",
        "\n",
        "# 1-step gradient descent.\n",
        "optimizer.step()\n",
        "\n",
        "# Print out the loss after 1-step gradient descent.\n",
        "pred = linear(x)\n",
        "loss = criterion(pred, y)\n",
        "print('loss after 1 step optimization: ', loss.item())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss: 1.4553918838500977\n",
            "dL/dw:  tensor([[-0.3979, -0.2021,  1.3191],\n",
            "        [-0.2496, -0.4713,  1.0693]])\n",
            "dL/db:  tensor([2.0722, 0.7060])\n",
            "loss after 1 step optimization:  1.4151173830032349\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcF9Mz38WxGy",
        "outputId": "f7a483cc-b404-46ce-858f-cc1ab26a8151"
      },
      "source": [
        "# Create a numpy array.\n",
        "x = np.array([[1, 2], [3, 4]])\n",
        "print(x)\n",
        "# Convert the numpy array to a torch tensor.\n",
        "y = torch.from_numpy(x)\n",
        "print(y)\n",
        "# Convert the torch tensor to a numpy array.\n",
        "z = y.numpy()\n",
        "print(z)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2]\n",
            " [3 4]]\n",
            "tensor([[1, 2],\n",
            "        [3, 4]])\n",
            "[[1 2]\n",
            " [3 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "11e12f8e04c949caac60821530281799",
            "73cfe544e16646fbbf0882bd1e027b02",
            "1c6dac357385409f84e29823c8de9d9f",
            "3fc5be4e043a40739890cb5d4d77cd90",
            "ab2190ad00074b61a728246952d3a7e8",
            "7dd3fdb3ecdb4d8c93ad06652d3f76d6",
            "512f667903804878a1b6445d990f6266",
            "eff55bc8dc104129b5953f888f8be846",
            "a66768ffcd6849398937b44a70abd0c9",
            "0c529674212e40c184fe0777e1c939a2",
            "b7f18730e1b5487fb551735f680b7d43"
          ]
        },
        "id": "nqYCp4UdXg3b",
        "outputId": "0d4f61c6-642b-44e8-b230-b6afa03a70fd"
      },
      "source": [
        "# Input pipeline\n",
        "# Download and construct CIFAR-10 dataset.\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
        "                                             train=True,\n",
        "                                             transform=transforms.ToTensor(),\n",
        "                                             download=True)\n",
        "# Fetch one data pair (read data from disk).\n",
        "image, label = train_dataset[0]\n",
        "print(image.size())\n",
        "print(label)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ../../data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11e12f8e04c949caac60821530281799",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../../data/cifar-10-python.tar.gz to ../../data/\n",
            "torch.Size([3, 32, 32])\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_ABaHe3YkzR"
      },
      "source": [
        "# Data loader (this provides queues and threads in a very simple way).\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=64,\n",
        "                                           shuffle=True)\n",
        "# When iteration starts, queue and thread start to load data from files.\n",
        "data_iter = iter(train_loader)\n",
        "\n",
        "# Mini-batch images and labels.\n",
        "images, labels = data_iter.next()\n",
        "print(images,labels)\n",
        "# Actual usage of the data loader is as below.\n",
        "for images, labels in train_loader:\n",
        "    # Training code should be written here.\n",
        "    \n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "WCN35WNKaHer",
        "outputId": "7c577683-eed3-41eb-87c1-776e7ea2b856"
      },
      "source": [
        "# Input pipeline for custom dataset\n",
        "# You should build your custom dataset as below.\n",
        "class CustomDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        # Initializer file paths or a list of file names.\n",
        "        pass\n",
        "    def __getitem__(self, index):\n",
        "        # Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).\n",
        "        # Preprocess the data (e.g. torchvision.Transform).\n",
        "        # Return a data pair (e.g. image and label).\n",
        "        pass\n",
        "    def __len__(self):\n",
        "        # You should change 0 to the total size of your dataset.\n",
        "        return 0\n",
        "# Youc an then use the prebuilt data loader.\n",
        "custom_dataset = CustomDataset()\n",
        "train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,\n",
        "                                           batch_size=64,\n",
        "                                           shuffle=True)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-1c54f2402b13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,\n\u001b[1;32m     18\u001b[0m                                            \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                                            shuffle=True)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn, multiprocessing_context, generator, prefetch_factor, persistent_workers)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# map-style\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m                     \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m                     \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequentialSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data_source, replacement, num_samples, generator)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m             raise ValueError(\"num_samples should be a positive integer \"\n\u001b[0;32m--> 103\u001b[0;31m                              \"value, but got num_samples={}\".format(self.num_samples))\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: num_samples should be a positive integer value, but got num_samples=0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "b8b31bd12aac49438a3a34aff2a3da65",
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            "162ce48604544a09ae2a923653899bfe",
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            "6e41311260ef45e3b4c7d0bbe7e5faf1",
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        },
        "id": "YYVOtTMBZduU",
        "outputId": "f025cd6a-a1b3-41e7-c8ce-a87164726d42"
      },
      "source": [
        "# Pretrained model\n",
        "# Download and load the pretrained ResNet-18.\n",
        "resnet = torchvision.models.resnet18(pretrained=True)\n",
        "\n",
        "# If you want to finetune only the top layer of the model, set as below.\n",
        "for param in resnet.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# Replace the top layer for finetuning.\n",
        "resnet.fc = nn.Linear(resnet.fc.in_features, 100) # 100 is an example.\n",
        "\n",
        "# Forward pass.\n",
        "images = torch.randn(64, 3, 224, 224)\n",
        "outputs = resnet(images)\n",
        "print(outputs.size()) "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8b31bd12aac49438a3a34aff2a3da65",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([64, 100])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6sHOTaXcrYw",
        "outputId": "264646fe-530d-40c1-f158-c3f976b5a40f"
      },
      "source": [
        "# Save and load the model\n",
        "# Save and load the entire model.\n",
        "torch.save(resnet, 'model.ckpt')\n",
        "model = torch.load('model.ckpt')\n",
        "\n",
        "# Save and load only the model parameters (recommended).\n",
        "torch.save(resnet.state_dict(), 'params.ckpt')\n",
        "resnet.load_state_dict(torch.load('params.ckpt'))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}