Split_Dataset_and_Baseline_Model.ipynb

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        "<a href=\"https://colab.research.google.com/github/abdulsam/MLOps_wandb/blob/main/Split_Dataset_and_Baseline_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "base_uri": "https://localhost:8080/"
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        "outputId": "3110c7b9-a876-4dd2-ce71-dc157b5c64b3"
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          ]
        }
      ],
      "source": [
        "# Install dependencies (run once)\n",
        "!wget https://raw.githubusercontent.com/wandb/edu/main/mlops-001/lesson1/requirements.txt\n",
        "!wget https://raw.githubusercontent.com/wandb/edu/main/mlops-001/lesson1/params.py\n",
        "!wget https://raw.githubusercontent.com/wandb/edu/main/mlops-001/lesson1/utils.py\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "WANDB_PROJECT = \"MLOps\"\n",
        "ENTITY = None # set this to team name if working in a team\n",
        "BDD_CLASSES = {i:c for i,c in enumerate(['background', 'road', 'traffic light', 'traffic sign', 'person', 'vehicle', 'bicycle'])}\n",
        "RAW_DATA_AT = 'bdd_simple_1k'\n",
        "PROCESSED_DATA_AT = 'bdd_simple_1k_split'"
      ],
      "metadata": {
        "id": "DGtVu-bB14cn"
      },
      "id": "DGtVu-bB14cn",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "e6cc5437-1a17-4738-a3c7-f1959324f4ed",
      "metadata": {
        "id": "e6cc5437-1a17-4738-a3c7-f1959324f4ed"
      },
      "source": [
        "# Data preparation\n",
        "\n",
        "<!--- @wandbcode{course-lesson1} -->\n",
        "\n",
        "In this notebook we will prepare the data to later train our deep learning model. To do so,\n",
        "\n",
        "- we will start a new W&B `run` and use our raw data artifact\n",
        "- split the data and save the splits into a new W&B Artifact\n",
        "- join information about the split with our EDA Table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "c8018ee4-8f0f-4e1d-a39d-d005bc9b4591",
      "metadata": {
        "id": "c8018ee4-8f0f-4e1d-a39d-d005bc9b4591"
      },
      "outputs": [],
      "source": [
        "import os, warnings\n",
        "import wandb\n",
        "\n",
        "import pandas as pd\n",
        "from fastai.vision.all import *\n",
        "from sklearn.model_selection import StratifiedGroupKFold\n",
        "\n",
        "import params\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "423709e6-1f8c-4b90-8a7b-6ed6c05492ca",
      "metadata": {
        "id": "423709e6-1f8c-4b90-8a7b-6ed6c05492ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "bb58b9c4-733d-4335-bcda-9fb383863171"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Finishing last run (ID:7xkrdfgb) before initializing another..."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">prime-planet-2</strong> at: <a href='https://wandb.ai/abdussamad/mlops-course-001/runs/7xkrdfgb' target=\"_blank\">https://wandb.ai/abdussamad/mlops-course-001/runs/7xkrdfgb</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230801_112236-7xkrdfgb/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Successfully finished last run (ID:7xkrdfgb). Initializing new run:<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.15.7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230801_112455-dngk3qfh</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/abdussamad/MLOps/runs/dngk3qfh' target=\"_blank\">stilted-flower-3</a></strong> to <a href='https://wandb.ai/abdussamad/MLOps' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/abdussamad/MLOps' target=\"_blank\">https://wandb.ai/abdussamad/MLOps</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/abdussamad/MLOps/runs/dngk3qfh' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/runs/dngk3qfh</a>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "run = wandb.init(project=WANDB_PROJECT, entity=ENTITY, job_type=\"data_split\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99b9419f-e0cc-4e57-b0f4-e9273fec996b",
      "metadata": {
        "id": "99b9419f-e0cc-4e57-b0f4-e9273fec996b"
      },
      "source": [
        "Let's use artifact we previously saved to W&B (we're storing artifact names and other global parameters in `params`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7841e7a1-188e-4021-9c2b-b662b7a13a08",
      "metadata": {
        "id": "7841e7a1-188e-4021-9c2b-b662b7a13a08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61991763-d4ec-4c87-ce14-4c521d00ada0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact bdd_simple_1k:latest, 813.75MB. 4007 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   4007 of 4007 files downloaded.  \n",
            "Done. 0:0:40.4\n"
          ]
        }
      ],
      "source": [
        "raw_data_at = run.use_artifact(f'{RAW_DATA_AT}:latest')\n",
        "path = Path(raw_data_at.download())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "160fcb5d-3a3b-40a6-b131-60b33ea3b530",
      "metadata": {
        "id": "160fcb5d-3a3b-40a6-b131-60b33ea3b530",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c832235a-64c9-4d48-8041-bfabb6e12d27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#5) [Path('artifacts/bdd_simple_1k:latest/images'),Path('artifacts/bdd_simple_1k:latest/eda_table.table.json'),Path('artifacts/bdd_simple_1k:latest/LICENSE.txt'),Path('artifacts/bdd_simple_1k:latest/labels'),Path('artifacts/bdd_simple_1k:latest/media')]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "path.ls()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fbbb8be-49cc-46c2-b563-b90d14892708",
      "metadata": {
        "id": "4fbbb8be-49cc-46c2-b563-b90d14892708"
      },
      "source": [
        "To split data between training, testing and validation, we need file names, groups (derived from the file name) and target (here we use our rare class bicycle for stratification). We previously saved these columns to EDA table, so let's retrieve it from the table now."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "156ac385-610f-43c8-ab3a-8ab91a1cbc06",
      "metadata": {
        "id": "156ac385-610f-43c8-ab3a-8ab91a1cbc06"
      },
      "outputs": [],
      "source": [
        "fnames = os.listdir(path/'images')\n",
        "groups = [s.split('-')[0] for s in fnames]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42d93918-c51e-4501-9b2a-9ea87ae1184a",
      "metadata": {
        "id": "42d93918-c51e-4501-9b2a-9ea87ae1184a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0626d17-8f7b-41cf-e548-83aa87a18a6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact bdd_simple_1k:latest, 813.75MB. 4007 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   4007 of 4007 files downloaded.  \n",
            "Done. 0:0:12.5\n"
          ]
        }
      ],
      "source": [
        "orig_eda_table = raw_data_at.get(\"eda_table\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a31eef6-725a-4eaa-9bb4-bec9aeaf4414",
      "metadata": {
        "id": "6a31eef6-725a-4eaa-9bb4-bec9aeaf4414"
      },
      "outputs": [],
      "source": [
        "y = orig_eda_table.get_column('bicycle')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "735730ed-e4c0-4ca6-82be-22a517cd74bc",
      "metadata": {
        "id": "735730ed-e4c0-4ca6-82be-22a517cd74bc"
      },
      "source": [
        "Now we will split the data into train (80%), validation (10%) and test (10%) sets. As we do that, we need to be careful to:\n",
        "\n",
        "- *avoid leakage*: for that reason we are grouping data according to video identifier (we want to make sure our model can generalize to new cars or video frames)\n",
        "- handle the *label imbalance*: for that reason we stratify data with our target column\n",
        "\n",
        "We will use sklearn's `StratifiedGroupKFold` to split the data into 10 folds and assign 1 fold for test, 1 for validation and the rest for training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1af79361-0aea-4ecd-bfaf-3bc034286cf5",
      "metadata": {
        "id": "1af79361-0aea-4ecd-bfaf-3bc034286cf5"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame()\n",
        "df['File_Name'] = fnames\n",
        "df['fold'] = -1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f09ceb74-d7dc-4a3b-9bca-7a30f3a1de23",
      "metadata": {
        "id": "f09ceb74-d7dc-4a3b-9bca-7a30f3a1de23"
      },
      "outputs": [],
      "source": [
        "cv = StratifiedGroupKFold(n_splits=10)\n",
        "for i, (train_idxs, test_idxs) in enumerate(cv.split(fnames, y, groups)):\n",
        "    df.loc[test_idxs, ['fold']] = i"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ced2899d-dc7e-4769-92e5-f0eaf585a4cf",
      "metadata": {
        "id": "ced2899d-dc7e-4769-92e5-f0eaf585a4cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cca046cd-1adf-4881-bf88-333579eb7212"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "train    800\n",
              "test     100\n",
              "valid    100\n",
              "Name: Stage, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df['Stage'] = 'train'\n",
        "df.loc[df.fold == 0, ['Stage']] = 'test'\n",
        "df.loc[df.fold == 1, ['Stage']] = 'valid'\n",
        "del df['fold']\n",
        "df.Stage.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b322c8d-dab6-4f21-8b0b-22fac2de3aae",
      "metadata": {
        "id": "3b322c8d-dab6-4f21-8b0b-22fac2de3aae"
      },
      "outputs": [],
      "source": [
        "df.to_csv('data_split.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b7fd8322-a52b-4f69-a716-04ce4b4ffb5b",
      "metadata": {
        "id": "b7fd8322-a52b-4f69-a716-04ce4b4ffb5b"
      },
      "source": [
        "We will now create a new artifact and add our data there."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7369b2a4-6c0b-44fb-8850-c8bfebd105ef",
      "metadata": {
        "id": "7369b2a4-6c0b-44fb-8850-c8bfebd105ef"
      },
      "outputs": [],
      "source": [
        "processed_data_at = wandb.Artifact(params.PROCESSED_DATA_AT, type=\"split_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b264b216-ef03-4417-88df-6fc27865c1f5",
      "metadata": {
        "id": "b264b216-ef03-4417-88df-6fc27865c1f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8969525e-fec0-454a-8621-73f628a86247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./artifacts/bdd_simple_1k:latest)... Done. 10.6s\n"
          ]
        }
      ],
      "source": [
        "processed_data_at.add_file('data_split.csv')\n",
        "processed_data_at.add_dir(path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7df4949c-c9de-4e0b-990e-58e98b2121e3",
      "metadata": {
        "id": "7df4949c-c9de-4e0b-990e-58e98b2121e3"
      },
      "source": [
        "Finally, the split information may be relevant for our analyses - rather than uploading images again, we will save the split information to a new table and join it with EDA table we created previously."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "818a359a-6b54-4f65-b5d9-732bbe5eb91a",
      "metadata": {
        "id": "818a359a-6b54-4f65-b5d9-732bbe5eb91a"
      },
      "outputs": [],
      "source": [
        "data_split_table = wandb.Table(dataframe=df[['File_Name', 'Stage']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eedc9255-2ee3-4015-bdd3-a8449a120895",
      "metadata": {
        "id": "eedc9255-2ee3-4015-bdd3-a8449a120895"
      },
      "outputs": [],
      "source": [
        "join_table = wandb.JoinedTable(orig_eda_table, data_split_table, \"File_Name\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "998b2369-d11b-4fef-a709-69cdc333ee1b",
      "metadata": {
        "id": "998b2369-d11b-4fef-a709-69cdc333ee1b"
      },
      "source": [
        "Let's add it to our artifact, log it and finish our `run`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42157919-05b2-4e28-8dc4-350160c0ae53",
      "metadata": {
        "id": "42157919-05b2-4e28-8dc4-350160c0ae53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b66b269-1fcc-445e-add7-044242abf745"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<wandb.sdk.artifacts.artifact_manifest_entry.ArtifactManifestEntry at 0x7efbc4a28340>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "processed_data_at.add(join_table, \"eda_table_data_split\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a6745c3-1ab6-4ebf-8f89-07d6da09fe65",
      "metadata": {
        "id": "1a6745c3-1ab6-4ebf-8f89-07d6da09fe65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "0e99b549-9244-4f6e-c75a-0ce0f943e46d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">stilted-flower-3</strong> at: <a href='https://wandb.ai/abdussamad/MLOps/runs/dngk3qfh' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/runs/dngk3qfh</a><br/> View job at <a href='https://wandb.ai/abdussamad/MLOps/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjg3MDk5OTk3/version_details/v0' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjg3MDk5OTk3/version_details/v0</a><br/>Synced 5 W&B file(s), 0 media file(s), 4042 artifact file(s) and 1 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230801_112455-dngk3qfh/logs</code>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "run.log_artifact(processed_data_at)\n",
        "run.finish()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#utils.py"
      ],
      "metadata": {
        "id": "2tt9rsY7ErS8"
      },
      "id": "2tt9rsY7ErS8"
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from IPython.display import display, Markdown\n",
        "from fastai.vision.all import *\n",
        "\n",
        "CLASS_INDEX = {v:k for k,v in BDD_CLASSES.items()}\n",
        "\n",
        "def iou_per_class(inp, targ):\n",
        "    \"Compute iou per class\"\n",
        "    iou_scores = []\n",
        "    for c in range(inp.shape[0]):\n",
        "        dec_preds = inp.argmax(dim=0)\n",
        "        p = torch.where(dec_preds == c, 1, 0)\n",
        "        t = torch.where(targ == c, 1, 0)\n",
        "        c_inter = (p * t).float().sum().item()\n",
        "        c_union = (p + t).float().sum().item()\n",
        "        iou_scores.append(c_inter / (c_union - c_inter) if c_union > 0 else np.nan)\n",
        "    return iou_scores\n",
        "\n",
        "def create_row(sample, pred_label, prediction, class_labels):\n",
        "    \"\"\"\"A simple function to create a row of (img, target, prediction, and scores...)\"\"\"\n",
        "    (image, label) = sample\n",
        "    # compute metrics\n",
        "    iou_scores = iou_per_class(prediction, label)\n",
        "    image = image.permute(1, 2, 0)\n",
        "    row =[wandb.Image(\n",
        "                image,\n",
        "                masks={\n",
        "                    \"predictions\": {\n",
        "                        \"mask_data\": pred_label[0].numpy(),\n",
        "                        \"class_labels\": class_labels,\n",
        "                    },\n",
        "                    \"ground_truths\": {\n",
        "                        \"mask_data\": label.numpy(),\n",
        "                        \"class_labels\": class_labels,\n",
        "                    },\n",
        "                },\n",
        "            ),\n",
        "            *iou_scores,\n",
        "    ]\n",
        "    return row\n",
        "\n",
        "def create_iou_table(samples, outputs, predictions, class_labels):\n",
        "    \"Creates a wandb table with predictions and targets side by side\"\n",
        "\n",
        "    def _to_str(l):\n",
        "        return [f'{str(x)} IoU' for x in l]\n",
        "\n",
        "    items = list(zip(samples, outputs, predictions))\n",
        "\n",
        "    table = wandb.Table(\n",
        "        columns=[\"Image\"]\n",
        "        + _to_str(class_labels.values()),\n",
        "    )\n",
        "    # we create one row per sample\n",
        "    for item in progress_bar(items):\n",
        "        table.add_data(*create_row(*item, class_labels=class_labels))\n",
        "\n",
        "    return table\n",
        "\n",
        "def get_predictions(learner, test_dl=None, max_n=None):\n",
        "    \"\"\"Return the samples = (x,y) and outputs (model predictions decoded), and predictions (raw preds)\"\"\"\n",
        "    test_dl = learner.dls.valid if test_dl is None else test_dl\n",
        "    inputs, predictions, targets, outputs = learner.get_preds(\n",
        "        dl=test_dl, with_input=True, with_decoded=True\n",
        "    )\n",
        "    x, y, samples, outputs = learner.dls.valid.show_results(\n",
        "        tuplify(inputs) + tuplify(targets), outputs, show=False, max_n=max_n\n",
        "    )\n",
        "    return samples, outputs, predictions\n",
        "\n",
        "    def value(self): return self.inter/(self.union-self.inter) if self.union > 0 else None\n",
        "\n",
        "class MIOU(DiceMulti):\n",
        "    @property\n",
        "    def value(self):\n",
        "        binary_iou_scores = np.array([])\n",
        "        for c in self.inter:\n",
        "            binary_iou_scores = np.append(binary_iou_scores, \\\n",
        "                                          self.inter[c]/(self.union[c]-self.inter[c]) if self.union[c] > 0 else np.nan)\n",
        "        return np.nanmean(binary_iou_scores)\n",
        "\n",
        "class IOU(DiceMulti):\n",
        "    @property\n",
        "    def value(self):\n",
        "        c=CLASS_INDEX[self.nm]\n",
        "        return self.inter[c]/(self.union[c]-self.inter[c]) if self.union[c] > 0 else np.nan\n",
        "\n",
        "class BackgroundIOU(IOU): nm = 'background'\n",
        "class RoadIOU(IOU): nm = 'road'\n",
        "class TrafficLightIOU(IOU): nm = 'traffic light'\n",
        "class TrafficSignIOU(IOU): nm = 'traffic sign'\n",
        "class PersonIOU(IOU): nm = 'person'\n",
        "class VehicleIOU(IOU): nm = 'vehicle'\n",
        "class BicycleIOU(IOU): nm = 'bicycle'\n",
        "\n",
        "\n",
        "class IOUMacro(DiceMulti):\n",
        "    @property\n",
        "    def value(self):\n",
        "        c=CLASS_INDEX[self.nm]\n",
        "        if c not in self.count: return np.nan\n",
        "        else: return self.macro[c]/self.count[c] if self.count[c] > 0 else np.nan\n",
        "\n",
        "    def reset(self): self.macro,self.count = {},{}\n",
        "\n",
        "    def accumulate(self, learn):\n",
        "        pred,targ = learn.pred.argmax(dim=self.axis), learn.y\n",
        "        for c in range(learn.pred.shape[self.axis]):\n",
        "            p = torch.where(pred == c, 1, 0)\n",
        "            t = torch.where(targ == c, 1, 0)\n",
        "            c_inter = (p*t).float().sum(dim=(1,2))\n",
        "            c_union = (p+t).float().sum(dim=(1,2))\n",
        "            m = c_inter / (c_union - c_inter)\n",
        "            macro = m[~torch.any(m.isnan())]\n",
        "            count = macro.shape[1]\n",
        "\n",
        "            if count > 0:\n",
        "                msum = macro.sum().item()\n",
        "                if c in self.count:\n",
        "                    self.count[c] += count\n",
        "                    self.macro[c] += msum\n",
        "                else:\n",
        "                    self.count[c] = count\n",
        "                    self.macro[c] = msum\n",
        "\n",
        "\n",
        "class MIouMacro(IOUMacro):\n",
        "    @property\n",
        "    def value(self):\n",
        "        binary_iou_scores = np.array([])\n",
        "        for c in self.count:\n",
        "            binary_iou_scores = np.append(binary_iou_scores, self.macro[c]/self.count[c] if self.count[c] > 0 else np.nan)\n",
        "        return np.nanmean(binary_iou_scores)\n",
        "\n",
        "\n",
        "class BackgroundIouMacro(IOUMacro): nm = 'background'\n",
        "class RoadIouMacro(IOUMacro): nm = 'road'\n",
        "class TrafficLightIouMacro(IOUMacro): nm = 'traffic light'\n",
        "class TrafficSignIouMacro(IOUMacro): nm = 'traffic sign'\n",
        "class PersonIouMacro(IOUMacro): nm = 'person'\n",
        "class VehicleIouMacro(IOUMacro): nm = 'vehicle'\n",
        "class BicycleIouMacro(IOUMacro): nm = 'bicycle'\n",
        "\n",
        "\n",
        "def display_diagnostics(learner, dls=None, return_vals=False):\n",
        "    \"\"\"\n",
        "    Display a confusion matrix for the unet learner.\n",
        "    If `dls` is None it will get the validation set from the Learner\n",
        "\n",
        "    You can create a test dataloader using the `test_dl()` method like so:\n",
        "    >> dls = ... # You usually create this from the DataBlocks api, in this library it is get_data()\n",
        "    >> tdls = dls.test_dl(test_dataframe, with_labels=True)\n",
        "\n",
        "    See: https://docs.fast.ai/tutorial.pets.html#adding-a-test-dataloader-for-inference\n",
        "\n",
        "    \"\"\"\n",
        "    probs, targs = learner.get_preds(dl = dls)\n",
        "    preds = probs.argmax(dim=1)\n",
        "    classes = list(params.BDD_CLASSES.values())\n",
        "    y_true = targs.flatten().numpy()\n",
        "    y_pred = preds.flatten().numpy()\n",
        "\n",
        "    tdf, pdf = [pd.DataFrame(r).value_counts().to_frame(c) for r,c in zip((y_true, y_pred) , ['y_true', 'y_pred'])]\n",
        "    countdf = tdf.join(pdf, how='outer').reset_index(drop=True).fillna(0).astype(int).rename(index= params.BDD_CLASSES)\n",
        "    countdf = countdf/countdf.sum()\n",
        "    display(Markdown('### % Of Pixels In Each Class'))\n",
        "    display(countdf.style.format('{:.1%}'))\n",
        "\n",
        "\n",
        "    disp = ConfusionMatrixDisplay.from_predictions(y_true=y_true, y_pred=y_pred,\n",
        "                                                   display_labels=classes,\n",
        "                                                   normalize='pred')\n",
        "    fig = disp.ax_.get_figure()\n",
        "    fig.set_figwidth(10)\n",
        "    fig.set_figheight(10)\n",
        "    disp.ax_.set_title('Confusion Matrix (by Pixels)', fontdict={'fontsize': 32, 'fontweight': 'medium'})\n",
        "    fig.show()\n",
        "\n",
        "    if return_vals: return countdf, disp\n",
        "\n"
      ],
      "metadata": {
        "id": "i6WYcuADEtsz"
      },
      "id": "i6WYcuADEtsz",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Baseline Model"
      ],
      "metadata": {
        "id": "YB7J3IrPEZE_"
      },
      "id": "YB7J3IrPEZE_"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "299d71f3",
      "metadata": {
        "id": "299d71f3"
      },
      "outputs": [],
      "source": [
        "import wandb\n",
        "import pandas as pd\n",
        "from fastai.vision.all import *\n",
        "from fastai.callback.wandb import WandbCallback"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_config = SimpleNamespace(\n",
        "    framework=\"fastai\",\n",
        "    img_size=(180, 320),\n",
        "    batch_size=8,\n",
        "    augment=True, # use data augmentation\n",
        "    epochs=10,\n",
        "    lr=2e-3,\n",
        "    pretrained=True,  # whether to use pretrained encoder\n",
        "    seed=42,\n",
        ")"
      ],
      "metadata": {
        "id": "mj0H0fs0E9AY"
      },
      "id": "mj0H0fs0E9AY",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_seed(train_config.seed, reproducible=True)"
      ],
      "metadata": {
        "id": "KiJZOk2jFBEW"
      },
      "id": "KiJZOk2jFBEW",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run = wandb.init(project=WANDB_PROJECT, entity=ENTITY, job_type=\"training\", config=train_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "HYRHA7B2FGbx",
        "outputId": "85eec1a7-d716-43b3-d7b5-59c5cc935053"
      },
      "id": "HYRHA7B2FGbx",
      "execution_count": 9,
      "outputs": [
        {
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            "application/javascript": [
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              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
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              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
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          "name": "stderr",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
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          "output_type": "display_data",
          "data": {
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              "Tracking run with wandb version 0.15.8"
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              "Syncing run <strong><a href='https://wandb.ai/abdussamad/MLOps/runs/dsj7335x' target=\"_blank\">clean-frog-5</a></strong> to <a href='https://wandb.ai/abdussamad/MLOps' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View run at <a href='https://wandb.ai/abdussamad/MLOps/runs/dsj7335x' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/runs/dsj7335x</a>"
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    {
      "cell_type": "code",
      "source": [
        "processed_data_at = run.use_artifact(f'{PROCESSED_DATA_AT}:latest')\n",
        "processed_dataset_dir = Path(processed_data_at.download())\n",
        "df = pd.read_csv(processed_dataset_dir / 'data_split.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwW-lo_hFJF0",
        "outputId": "5ac4c82f-e265-4f12-92d1-e69a312d1dd5"
      },
      "id": "VwW-lo_hFJF0",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact bdd_simple_1k_split:latest, 813.25MB. 4010 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   4010 of 4010 files downloaded.  \n",
            "Done. 0:0:40.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df.Stage != 'test'].reset_index(drop=True)\n",
        "df['is_valid'] = df.Stage == 'valid'"
      ],
      "metadata": {
        "id": "yA4fNkMiFVBS"
      },
      "id": "yA4fNkMiFVBS",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def label_func(fname):\n",
        "    return (fname.parent.parent/\"labels\")/f\"{fname.stem}_mask.png\""
      ],
      "metadata": {
        "id": "oSYqzU-_GRv9"
      },
      "id": "oSYqzU-_GRv9",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# assign paths\n",
        "df[\"image_fname\"] = [processed_dataset_dir/f'images/{f}' for f in df.File_Name.values]\n",
        "df[\"label_fname\"] = [label_func(f) for f in df.image_fname.values]"
      ],
      "metadata": {
        "id": "MmGdcQSlGVlY"
      },
      "id": "MmGdcQSlGVlY",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_data(df, bs=4, img_size=(180, 320), augment=True):\n",
        "    block = DataBlock(blocks=(ImageBlock, MaskBlock(codes=BDD_CLASSES)),\n",
        "                  get_x=ColReader(\"image_fname\"),\n",
        "                  get_y=ColReader(\"label_fname\"),\n",
        "                  splitter=ColSplitter(),\n",
        "                  item_tfms=Resize(img_size),\n",
        "                  batch_tfms=aug_transforms() if augment else None,\n",
        "                 )\n",
        "    return block.dataloaders(df, bs=bs)"
      ],
      "metadata": {
        "id": "ku4v-7AsGiCL"
      },
      "id": "ku4v-7AsGiCL",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = wandb.config"
      ],
      "metadata": {
        "id": "idISSBJPGmeE"
      },
      "id": "idISSBJPGmeE",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dls = get_data(df, bs=config.batch_size, img_size=config.img_size, augment=config.augment)"
      ],
      "metadata": {
        "id": "o3MfvA7yGozG"
      },
      "id": "o3MfvA7yGozG",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = [MIOU(), BackgroundIOU(), RoadIOU(), TrafficLightIOU(), \\\n",
        "           TrafficSignIOU(), PersonIOU(), VehicleIOU(), BicycleIOU()]\n",
        "\n",
        "learn = unet_learner(dls, arch=resnet18, pretrained=config.pretrained, metrics=metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95prM4etGq4A",
        "outputId": "db9adcfe-f4a8-4980-c5a3-edb0199a6ddb"
      },
      "id": "95prM4etGq4A",
      "execution_count": 17,
      "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",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 65.9MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "    SaveModelCallback(monitor='miou'),\n",
        "    WandbCallback(log_preds=False, log_model=True)\n",
        "]"
      ],
      "metadata": {
        "id": "AqNIuMQHG6qv"
      },
      "id": "AqNIuMQHG6qv",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(config.epochs, config.lr, cbs=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "RbJTA59rG-3U",
        "outputId": "e6eae98c-7e09-4648-8918-2196a67003c2"
      },
      "id": "RbJTA59rG-3U",
      "execution_count": 19,
      "outputs": [
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              "      <progress value='5' class='' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      50.00% [5/10 03:34&lt;03:34]\n",
              "    </div>\n",
              "    \n",
              "<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>miou</th>\n",
              "      <th>background_iou</th>\n",
              "      <th>road_iou</th>\n",
              "      <th>traffic_light_iou</th>\n",
              "      <th>traffic_sign_iou</th>\n",
              "      <th>person_iou</th>\n",
              "      <th>vehicle_iou</th>\n",
              "      <th>bicycle_iou</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.503500</td>\n",
              "      <td>0.364788</td>\n",
              "      <td>0.302899</td>\n",
              "      <td>0.856045</td>\n",
              "      <td>0.682933</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.581317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:44</td>\n",
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              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.441540</td>\n",
              "      <td>0.456096</td>\n",
              "      <td>0.223625</td>\n",
              "      <td>0.804077</td>\n",
              "      <td>0.752603</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008696</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.341107</td>\n",
              "      <td>0.310370</td>\n",
              "      <td>0.335007</td>\n",
              "      <td>0.889018</td>\n",
              "      <td>0.781805</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.674224</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.308571</td>\n",
              "      <td>0.294588</td>\n",
              "      <td>0.335348</td>\n",
              "      <td>0.888502</td>\n",
              "      <td>0.773587</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.685345</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.284449</td>\n",
              "      <td>0.295170</td>\n",
              "      <td>0.337692</td>\n",
              "      <td>0.890041</td>\n",
              "      <td>0.753821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.719981</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "      <progress value='50' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      50.00% [50/100 00:19&lt;00:19 0.2634]\n",
              "    </div>\n",
              "    "
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        {
          "metadata": {
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          },
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          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with miou value: 0.30289944952858733.\n",
            "Better model found at epoch 2 with miou value: 0.33500671276999844.\n",
            "Better model found at epoch 3 with miou value: 0.3353476620971991.\n",
            "Better model found at epoch 4 with miou value: 0.33769184654925394.\n"
          ]
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        {
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              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>miou</th>\n",
              "      <th>background_iou</th>\n",
              "      <th>road_iou</th>\n",
              "      <th>traffic_light_iou</th>\n",
              "      <th>traffic_sign_iou</th>\n",
              "      <th>person_iou</th>\n",
              "      <th>vehicle_iou</th>\n",
              "      <th>bicycle_iou</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.503500</td>\n",
              "      <td>0.364788</td>\n",
              "      <td>0.302899</td>\n",
              "      <td>0.856045</td>\n",
              "      <td>0.682933</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.581317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.441540</td>\n",
              "      <td>0.456096</td>\n",
              "      <td>0.223625</td>\n",
              "      <td>0.804077</td>\n",
              "      <td>0.752603</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008696</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.341107</td>\n",
              "      <td>0.310370</td>\n",
              "      <td>0.335007</td>\n",
              "      <td>0.889018</td>\n",
              "      <td>0.781805</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.674224</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.308571</td>\n",
              "      <td>0.294588</td>\n",
              "      <td>0.335348</td>\n",
              "      <td>0.888502</td>\n",
              "      <td>0.773587</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.685345</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.284449</td>\n",
              "      <td>0.295170</td>\n",
              "      <td>0.337692</td>\n",
              "      <td>0.890041</td>\n",
              "      <td>0.753821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.719981</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.244119</td>\n",
              "      <td>0.291671</td>\n",
              "      <td>0.347504</td>\n",
              "      <td>0.902776</td>\n",
              "      <td>0.799168</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.730585</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.226735</td>\n",
              "      <td>0.267549</td>\n",
              "      <td>0.356690</td>\n",
              "      <td>0.909136</td>\n",
              "      <td>0.822833</td>\n",
              "      <td>0.019914</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.744949</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.206693</td>\n",
              "      <td>0.256822</td>\n",
              "      <td>0.360256</td>\n",
              "      <td>0.910406</td>\n",
              "      <td>0.823629</td>\n",
              "      <td>0.038498</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.749256</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.191840</td>\n",
              "      <td>0.244747</td>\n",
              "      <td>0.367153</td>\n",
              "      <td>0.912149</td>\n",
              "      <td>0.825660</td>\n",
              "      <td>0.075743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.756516</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.179307</td>\n",
              "      <td>0.243743</td>\n",
              "      <td>0.365229</td>\n",
              "      <td>0.912416</td>\n",
              "      <td>0.826369</td>\n",
              "      <td>0.063608</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.754213</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>00:41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 5 with miou value: 0.3475041149280384.\n",
            "Better model found at epoch 6 with miou value: 0.3566903117209734.\n",
            "Better model found at epoch 7 with miou value: 0.36025550714110466.\n",
            "Better model found at epoch 8 with miou value: 0.3671525157565793.\n"
          ]
        }
      ]
    },
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        "samples, outputs, predictions = get_predictions(learn)\n",
        "table = create_iou_table(samples, outputs, predictions, BDD_CLASSES)\n",
        "wandb.log({\"pred_table\":table})"
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        "    wandb.summary[k] = v"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>background_iou</td><td>▄▁▆▆▇▇████</td></tr><tr><td>bicycle_iou</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_2</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▂▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▁▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▂▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▁▁▁▁▁▁</td></tr><tr><td>lr_2</td><td>▁▂▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▁▁▁▁▁▁</td></tr><tr><td>miou</td><td>▅▁▆▆▇▇▇███</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██████</td></tr><tr><td>mom_2</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██████</td></tr><tr><td>person_iou</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>raw_loss</td><td>█▄▅▄▄▃▄▆▄▂▂▂▂▃▄▃▂▃▂▂▂▂▂▂▂▂▁▂▂▁▁▂▁▃▁▂▂▁▁▁</td></tr><tr><td>road_iou</td><td>▁▄▆▅▄▇████</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_2</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>traffic_light_iou</td><td>▁▁▁▁▁▁▃▅█▇</td></tr><tr><td>traffic_sign_iou</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▅▄▃▃▃▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_samples_per_sec</td><td>█▇▅▇▆▆▄▅▄▄▂▄▁▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▄▅▅▅▅</td></tr><tr><td>valid_loss</td><td>▅█▃▃▃▃▂▁▁▁</td></tr><tr><td>vehicle_iou</td><td>▆▁▇▇██████</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_2</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>background_iou</td><td>0.91242</td></tr><tr><td>bicycle_iou</td><td>0.0</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>eps_2</td><td>1e-05</td></tr><tr><td>final_background_iou</td><td>0.91215</td></tr><tr><td>final_bicycle_iou</td><td>0.0</td></tr><tr><td>final_loss</td><td>0.24475</td></tr><tr><td>final_miou</td><td>0.36715</td></tr><tr><td>final_person_iou</td><td>0.0</td></tr><tr><td>final_road_iou</td><td>0.82566</td></tr><tr><td>final_traffic_light_iou</td><td>0.07574</td></tr><tr><td>final_traffic_sign_iou</td><td>0.0</td></tr><tr><td>final_vehicle_iou</td><td>0.75652</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>lr_2</td><td>0.0</td></tr><tr><td>miou</td><td>0.36523</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>mom_2</td><td>0.95</td></tr><tr><td>person_iou</td><td>0.0</td></tr><tr><td>raw_loss</td><td>0.23046</td></tr><tr><td>road_iou</td><td>0.82637</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>sqr_mom_2</td><td>0.99</td></tr><tr><td>traffic_light_iou</td><td>0.06361</td></tr><tr><td>traffic_sign_iou</td><td>0.0</td></tr><tr><td>train_loss</td><td>0.17931</td></tr><tr><td>train_samples_per_sec</td><td>24.3538</td></tr><tr><td>valid_loss</td><td>0.24374</td></tr><tr><td>vehicle_iou</td><td>0.75421</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr><tr><td>wd_2</td><td>0.01</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">clean-frog-5</strong> at: <a href='https://wandb.ai/abdussamad/MLOps/runs/dsj7335x' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/runs/dsj7335x</a><br/> View job at <a href='https://wandb.ai/abdussamad/MLOps/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjg3NjYzMTgw/version_details/v0' target=\"_blank\">https://wandb.ai/abdussamad/MLOps/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjg3NjYzMTgw/version_details/v0</a><br/>Synced 5 W&B file(s), 1 media file(s), 319 artifact file(s) and 1 other file(s)"
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