Lesson1_02_saving_a_basic_fastai_model.ipynb

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        "<a href=\"https://colab.research.google.com/github/isa-ulisboa/greends-aml/blob/main/Lesson1_02_saving_a_basic_fastai_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Saving a Cats v Dogs Model"
      ],
      "metadata": {
        "id": "98d53c05"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a minimal example showing how to train a fastai model on Kaggle, and save it so you can use it in your app.\n",
        "\n",
        "mlc: It has been modified to run on Colab"
      ],
      "metadata": {
        "id": "-ynogTarwPna"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Colab instead of Kaggle <--------------------------------------------------------------------\n",
        "#hide\n",
        "! [ -e /content ] && pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()\n",
        "     "
      ],
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        "id": "evvA0fqvSblq",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hMounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastbook import *\n",
        "     "
      ],
      "metadata": {
        "id": "vSATOcNpxVIE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, import all the stuff we need from fastai:"
      ],
      "metadata": {
        "id": "_lvHk7zvwPnc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.vision.all import *"
      ],
      "metadata": {
        "id": "44eb0ad3",
        "execution": {
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      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Download and decompress our dataset, which is pictures of dogs and cats:"
      ],
      "metadata": {
        "id": "3Ld5iIlmwPnc"
      }
    },
    {
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        "path = untar_data(URLs.PETS)/'images'"
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    {
      "cell_type": "code",
      "source": [
        "path"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IWakokq0TXM",
        "outputId": "a42d785d-fd18-494e-d574-486606074563"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/root/.fastai/data/oxford-iiit-pet/images')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need a way to label our images as dogs or cats. In this dataset, pictures of cats are given a filename that starts with a capital letter:"
      ],
      "metadata": {
        "id": "pvz6zS7gwPnd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def is_cat(x): return x[0].isupper() "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-05-03T05:53:02.467289Z",
          "iopub.execute_input": "2022-05-03T05:53:02.467572Z",
          "iopub.status.idle": "2022-05-03T05:53:02.474701Z",
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          "shell.execute_reply": "2022-05-03T05:53:02.474109Z"
        },
        "trusted": true,
        "id": "thmv2gAywPne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can create our `DataLoaders`:"
      ],
      "metadata": {
        "id": "qfA9XRiNwPne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dls = ImageDataLoaders.from_name_func('.',\n",
        "    get_image_files(path), valid_pct=0.2, seed=42,\n",
        "    label_func=is_cat,\n",
        "    item_tfms=Resize(192))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-05-03T05:53:02.475754Z",
          "iopub.execute_input": "2022-05-03T05:53:02.476084Z",
          "iopub.status.idle": "2022-05-03T05:53:06.703777Z",
          "shell.execute_reply.started": "2022-05-03T05:53:02.476052Z",
          "shell.execute_reply": "2022-05-03T05:53:06.703023Z"
        },
        "trusted": true,
        "id": "tgkFWW6awPnf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... and train our model, a resnet18 (to keep it small and fast):"
      ],
      "metadata": {
        "id": "C002J8pTwPnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
        "learn.fine_tune(3)"
      ],
      "metadata": {
        "id": "c107f724",
        "execution": {
          "iopub.status.busy": "2022-05-03T05:53:28.092381Z",
          "iopub.execute_input": "2022-05-03T05:53:28.093059Z"
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          "referenced_widgets": [
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        "outputId": "4095c43a-6725-426b-a86e-a69e4edd170a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
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              "version_major": 2,
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.194672</td>\n",
              "      <td>0.055344</td>\n",
              "      <td>0.015562</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
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              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.069598</td>\n",
              "      <td>0.023813</td>\n",
              "      <td>0.008119</td>\n",
              "      <td>00:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.042776</td>\n",
              "      <td>0.019394</td>\n",
              "      <td>0.008119</td>\n",
              "      <td>01:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.024227</td>\n",
              "      <td>0.018034</td>\n",
              "      <td>0.006766</td>\n",
              "      <td>00:59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can export our trained `Learner`. This contains all the information needed to run the model:"
      ],
      "metadata": {
        "id": "VAJaUL9HwPnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learn.export('model.pkl')"
      ],
      "metadata": {
        "id": "ae2bc6ac",
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, open the Kaggle sidebar on the right if it's not already, and find the section marked \"Output\". Open the `/kaggle/working` folder, and you'll see `model.pkl`. Click on it, then click on the menu on the right that appears, and choose \"Download\". After a few seconds, your model will be downloaded to your computer, where you can then create your app that uses the model."
      ],
      "metadata": {
        "id": "Q2HTrQKTf3BV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In Colab, the file 'model.pkl' will be saved on the root. But you can move it to your google drive, or you can adapt the command to save it there:"
      ],
      "metadata": {
        "id": "pYyoPYho5GVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learn.export('/content/gdrive/MyDrive/AAA/02-saving-a-basic-fastai-model/model.pkl')"
      ],
      "metadata": {
        "trusted": true,
        "id": "MWCU1hVP3EPB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}