Копия_блокнота_"Fine_Tuning_SAM_ipynb".ipynb

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      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NodiraTillayeva/ArcGis/blob/main/%D0%9A%D0%BE%D0%BF%D0%B8%D1%8F_%D0%B1%D0%BB%D0%BE%D0%BA%D0%BD%D0%BE%D1%82%D0%B0_%22Fine_Tuning_SAM_ipynb%22.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overview\n",
        "This tutorial has been prepared by Alex Bonnet, ML Solutions Engineer @ Encord - you might also be interested in checking out our open-source framework for computer vision model testing, evaluation, and validation that you can find here https://github.com/encord-team/encord-active).\n",
        "\n",
        "This is the notebook which implements the steps discussed in this blog post: https://encord.com/blog/learn-how-to-fine-tune-the-segment-anything-model-sam/\n",
        "\n",
        "The aim is to take Segment Anything Model and fine tune it to a specific application. We are going to be using the stamp verification dataset https://www.kaggle.com/datasets/rtatman/stamp-verification-staver-dataset since it has data SAM is unlikely to have seen (scans of invoices with stamps), has precise ground truth segmentation masks and also has bounding boxes which we can use as prompts to SAM."
      ],
      "metadata": {
        "id": "aiehQvBj5Crc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up"
      ],
      "metadata": {
        "id": "8xFZSDikKMsA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "r0oru8hAn6q2"
      },
      "outputs": [],
      "source": [
        "! pip install torch torchvision &> /dev/null\n",
        "! pip install opencv-python pycocotools matplotlib onnxruntime onnx &> /dev/null\n",
        "! pip install git+https://github.com/facebookresearch/segment-anything.git &> /dev/null\n",
        "! wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth &> /dev/null"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Action Required:** Place your kaggle.json file into the files in the notebook workspace. More info here https://github.com/Kaggle/kaggle-api#api-credentials"
      ],
      "metadata": {
        "id": "YkwevXNd2Ofw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip datasetPng.zip &> /dev/null"
      ],
      "metadata": {
        "id": "zJP-eL2_EA52"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2"
      ],
      "metadata": {
        "id": "lwmQm0C3n_3D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocess data"
      ],
      "metadata": {
        "id": "WNJGn1BsKUMS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We extract the bounding box coordinates which will be used to feed into SAM as prompts."
      ],
      "metadata": {
        "id": "KhXGI8HXyIFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bbox_coords = {}\n",
        "for f in sorted(Path('datasetPng/ground-truth-maps/ground-truth-maps/').iterdir())[:50]:\n",
        "  k = f.stem\n",
        "  im = cv2.imread(f.as_posix())\n",
        "  gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
        "  contours, hierarchy = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[-2:]\n",
        "  print(len(contours))\n",
        "  if len(contours) > 0:\n",
        "    x,y,w,h = cv2.boundingRect(contours[0])\n",
        "    height, width, _ = im.shape\n",
        "    bbox_coords[k] = np.array([x, y, x + w, y + h])\n",
        "print(bbox_coords)"
      ],
      "metadata": {
        "id": "PNrV8CN8F9G0",
        "outputId": "01445b03-19ce-406d-a437-8255265bbf11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
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        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "We extract the ground truth segmentation masks"
      ],
      "metadata": {
        "id": "zsv0IGBDyMkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ground_truth_masks = {}\n",
        "for k in bbox_coords.keys():\n",
        "  gt_grayscale = cv2.imread(f'datasetPng/ground-truth-pixel/ground-truth-pixel/{k}.png', cv2.IMREAD_GRAYSCALE)\n",
        "  print(k)\n",
        "  print(gt_grayscale)\n",
        "  ground_truth_masks[k] = (gt_grayscale == 0)"
      ],
      "metadata": {
        "id": "lz7B4NDoJRxJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de2348b3-0cdf-4f4d-fa80-0cd2292c54da"
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            " ...\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]]\n",
            "000000000045\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "000000000046\n",
            "[[  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " ...\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]]\n",
            "000000000047\n",
            "[[  0   0   0 ...   0   0   0]\n",
            " [  0   0   0 ...   0   0   0]\n",
            " [  0   0   0 ...   0   0   0]\n",
            " ...\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]]\n",
            "000000000048\n",
            "[[  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " ...\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]]\n",
            "000000000049\n",
            "[[  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " [  0   0   0 ... 255 255 255]\n",
            " ...\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]]\n",
            "000000000050\n",
            "[[255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " ...\n",
            " [255 255 255 ...   0   0   0]\n",
            " [255 255 255 ...   0   0   0]\n",
            " [255 255 255 ...   0   0   0]]\n",
            "000000000051\n",
            "[[255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " ...\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]]\n",
            "000000000052\n",
            "[[255 255 255 ...   0   0   0]\n",
            " [255 255 255 ...   0   0   0]\n",
            " [255 255 255 ...   0   0   0]\n",
            " ...\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]]\n",
            "000000000054\n",
            "[[255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " ...\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]\n",
            " [255 255 255 ... 255 255 255]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Take a look at the images, the bounding box prompts and the ground truth segmentation masks"
      ],
      "metadata": {
        "id": "FsX7SxD8KYOP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper functions provided in https://github.com/facebookresearch/segment-anything/blob/9e8f1309c94f1128a6e5c047a10fdcb02fc8d651/notebooks/predictor_example.ipynb\n",
        "def show_mask(mask, ax, random_color=False):\n",
        "    if random_color:\n",
        "        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n",
        "    else:\n",
        "        color = np.array([30/255, 144/255, 255/255, 0.6])\n",
        "    h, w = mask.shape[-2:]\n",
        "    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
        "    ax.imshow(mask_image)\n",
        "\n",
        "def show_box(box, ax):\n",
        "    x0, y0 = box[0], box[1]\n",
        "    w, h = box[2] - box[0], box[3] - box[1]\n",
        "    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))"
      ],
      "metadata": {
        "id": "Bz8C8QaxoT6N"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see here that the ground truth mask is extremely tight which will be good for calculating an accurate loss.\n",
        "The bounding box overlaid will be a good prompt."
      ],
      "metadata": {
        "id": "ncVrlh5fyed9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "name = '000000000052'\n",
        "image = cv2.imread(f'datasetPng/scans/scans/{name}.png')\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.imshow(image)\n",
        "show_mask(ground_truth_masks[name], plt.gca())\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3csOAxFju_Pi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "b87bd9c0-65e2-4234-fcd3-7745c25f560b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Fine Tuning"
      ],
      "metadata": {
        "id": "IJIFDGaUKfQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_type = 'vit_b'\n",
        "checkpoint = 'sam_vit_b_01ec64.pth'\n",
        "device = 'cuda:0'"
      ],
      "metadata": {
        "id": "OdTD9CTxKena"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from segment_anything import SamPredictor, sam_model_registry\n",
        "sam_model = sam_model_registry[model_type](checkpoint=checkpoint)\n",
        "sam_model.to(device)\n",
        "sam_model.train();"
      ],
      "metadata": {
        "id": "HjTIJtLxP8ZG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We convert the input images into a format SAM's internal functions expect."
      ],
      "metadata": {
        "id": "Bu0MdejGylZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the images\n",
        "from collections import defaultdict\n",
        "import os\n",
        "import torch\n",
        "\n",
        "from segment_anything.utils.transforms import ResizeLongestSide\n",
        "\n",
        "transformed_data = defaultdict(dict)\n",
        "for k in bbox_coords.keys():\n",
        "  print(k)\n",
        "  image_path = f'datasetPng/scans/scans/{k}.png'\n",
        "\n",
        "  if not os.path.exists(image_path):\n",
        "    print(f\"Image not found: {image_path}\")\n",
        "    continue\n",
        "\n",
        "  image = cv2.imread(image_path)\n",
        "  if image is None:\n",
        "    print(f\"Empty file: {image_path}\")\n",
        "    continue\n",
        "\n",
        "  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  transform = ResizeLongestSide(sam_model.image_encoder.img_size)\n",
        "  input_image = transform.apply_image(image)\n",
        "  input_image_torch = torch.as_tensor(input_image, device=device)\n",
        "  transformed_image = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n",
        "\n",
        "  input_image = sam_model.preprocess(transformed_image)\n",
        "  original_image_size = image.shape[:2]\n",
        "  input_size = tuple(transformed_image.shape[-2:])\n",
        "\n",
        "  transformed_data[k]['image'] = input_image\n",
        "  transformed_data[k]['input_size'] = input_size\n",
        "  transformed_data[k]['original_image_size'] = original_image_size"
      ],
      "metadata": {
        "id": "jtPYpirbK3Wi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86d120d5-daaa-4170-e1b5-f7b89e359fff"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "000000000000\n",
            "000000000001\n",
            "000000000002\n",
            "000000000003\n",
            "000000000004\n",
            "000000000005\n",
            "000000000006\n",
            "000000000007\n",
            "000000000008\n",
            "000000000009\n",
            "000000000010\n",
            "000000000011\n",
            "000000000012\n",
            "000000000013\n",
            "000000000014\n",
            "000000000018\n",
            "000000000019\n",
            "000000000020\n",
            "000000000021\n",
            "000000000022\n",
            "000000000023\n",
            "000000000024\n",
            "000000000025\n",
            "000000000026\n",
            "000000000027\n",
            "000000000028\n",
            "000000000029\n",
            "000000000030\n",
            "000000000031\n",
            "000000000032\n",
            "000000000033\n",
            "000000000034\n",
            "Image not found: datasetPng/scans/scans/000000000034.png\n",
            "000000000036\n",
            "000000000037\n",
            "000000000038\n",
            "000000000039\n",
            "000000000040\n",
            "000000000041\n",
            "000000000042\n",
            "000000000043\n",
            "000000000044\n",
            "000000000045\n",
            "000000000046\n",
            "000000000047\n",
            "000000000048\n",
            "000000000049\n",
            "000000000050\n",
            "000000000051\n",
            "000000000052\n",
            "000000000054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the optimizer, hyperparameter tuning will improve performance here\n",
        "lr = 1e-4\n",
        "wd = 0.001\n",
        "optimizer = torch.optim.Adam(sam_model.mask_decoder.parameters(), lr=lr, weight_decay=wd)\n",
        "\n",
        "loss_fn = torch.nn.MSELoss()\n",
        "# loss_fn = torch.nn.BCELoss()\n",
        "keys = list(bbox_coords.keys())"
      ],
      "metadata": {
        "id": "QxnY6TMGKjdc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run fine tuning"
      ],
      "metadata": {
        "id": "sRHCNdzZy3dt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the main training loop.\n",
        "\n",
        "Improvements to be made include batching and moving the computation of the image and prompt embeddings outside the loop since we are not tuning these parts of the model, this will speed up training as we should not recompute the embeddings during each epoch. Sometimes the optimizer gets lost in the parameter space and the loss function blows up. Restarting from scratch (including running all cells below 'Prepare Fine Tuning' in order to start with default weights again) will solve this.\n",
        "\n",
        "In a production implementation a better choice of optimiser/loss function will certainly help."
      ],
      "metadata": {
        "id": "9DIYcFKu14nr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import mean\n",
        "\n",
        "from tqdm import tqdm\n",
        "from torch.nn.functional import threshold, normalize\n",
        "\n",
        "num_epochs = 100\n",
        "losses = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  epoch_losses = []\n",
        "  # Just train on the first 20 examples\n",
        "  for k in keys[:20]:\n",
        "    input_image = transformed_data[k]['image'].to(device)\n",
        "    input_size = transformed_data[k]['input_size']\n",
        "    original_image_size = transformed_data[k]['original_image_size']\n",
        "\n",
        "    # No grad here as we don't want to optimise the encoders\n",
        "    with torch.no_grad():\n",
        "      image_embedding = sam_model.image_encoder(input_image)\n",
        "\n",
        "      prompt_box = bbox_coords[k]\n",
        "      box = transform.apply_boxes(prompt_box, original_image_size)\n",
        "      box_torch = torch.as_tensor(box, dtype=torch.float, device=device)\n",
        "      box_torch = box_torch[None, :]\n",
        "\n",
        "      sparse_embeddings, dense_embeddings = sam_model.prompt_encoder(\n",
        "          points=None,\n",
        "          boxes=box_torch,\n",
        "          masks=None,\n",
        "      )\n",
        "    low_res_masks, iou_predictions = sam_model.mask_decoder(\n",
        "      image_embeddings=image_embedding,\n",
        "      image_pe=sam_model.prompt_encoder.get_dense_pe(),\n",
        "      sparse_prompt_embeddings=sparse_embeddings,\n",
        "      dense_prompt_embeddings=dense_embeddings,\n",
        "      multimask_output=False,\n",
        "    )\n",
        "\n",
        "    upscaled_masks = sam_model.postprocess_masks(low_res_masks, input_size, original_image_size).to(device)\n",
        "    binary_mask = normalize(threshold(upscaled_masks, 0.0, 0))\n",
        "\n",
        "    gt_mask_resized = torch.from_numpy(np.resize(ground_truth_masks[k], (1, 1, ground_truth_masks[k].shape[0], ground_truth_masks[k].shape[1]))).to(device)\n",
        "    gt_binary_mask = torch.as_tensor(gt_mask_resized > 0, dtype=torch.float32)\n",
        "\n",
        "    loss = loss_fn(binary_mask, gt_binary_mask)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    epoch_losses.append(loss.item())\n",
        "  losses.append(epoch_losses)\n",
        "  print(f'EPOCH: {epoch}')\n",
        "  print(f'Mean loss: {mean(epoch_losses)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRQ6yd_PM_B9",
        "outputId": "295c65f0-fe69-412e-f172-f612b50c0e11"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH: 0\n",
            "Mean loss: 0.518511962890625\n",
            "EPOCH: 1\n",
            "Mean loss: 0.48515853881835935\n",
            "EPOCH: 2\n",
            "Mean loss: 0.4069252014160156\n",
            "EPOCH: 3\n",
            "Mean loss: 0.3543998718261719\n",
            "EPOCH: 4\n",
            "Mean loss: 0.3430198669433594\n",
            "EPOCH: 5\n",
            "Mean loss: 0.34072723388671877\n",
            "EPOCH: 6\n",
            "Mean loss: 0.33989944458007815\n",
            "EPOCH: 7\n",
            "Mean loss: 0.339605712890625\n",
            "EPOCH: 8\n",
            "Mean loss: 0.33945693969726565\n",
            "EPOCH: 9\n",
            "Mean loss: 0.33954620361328125\n",
            "EPOCH: 10\n",
            "Mean loss: 0.3397003173828125\n",
            "EPOCH: 11\n",
            "Mean loss: 0.3399177551269531\n",
            "EPOCH: 12\n",
            "Mean loss: 0.34015655517578125\n",
            "EPOCH: 13\n",
            "Mean loss: 0.34034423828125\n",
            "EPOCH: 14\n",
            "Mean loss: 0.34058151245117185\n",
            "EPOCH: 15\n",
            "Mean loss: 0.34099807739257815\n",
            "EPOCH: 16\n",
            "Mean loss: 0.34151763916015626\n",
            "EPOCH: 17\n",
            "Mean loss: 0.3421806335449219\n",
            "EPOCH: 18\n",
            "Mean loss: 0.3427642822265625\n",
            "EPOCH: 19\n",
            "Mean loss: 0.3432350158691406\n",
            "EPOCH: 20\n",
            "Mean loss: 0.34374847412109377\n",
            "EPOCH: 21\n",
            "Mean loss: 0.34422149658203127\n",
            "EPOCH: 22\n",
            "Mean loss: 0.3447837829589844\n",
            "EPOCH: 23\n",
            "Mean loss: 0.3453216552734375\n",
            "EPOCH: 24\n",
            "Mean loss: 0.34591827392578123\n",
            "EPOCH: 25\n",
            "Mean loss: 0.34650344848632814\n",
            "EPOCH: 26\n",
            "Mean loss: 0.3470802307128906\n",
            "EPOCH: 27\n",
            "Mean loss: 0.34771041870117186\n",
            "EPOCH: 28\n",
            "Mean loss: 0.34833755493164065\n",
            "EPOCH: 29\n",
            "Mean loss: 0.34908447265625\n",
            "EPOCH: 30\n",
            "Mean loss: 0.34990615844726564\n",
            "EPOCH: 31\n",
            "Mean loss: 0.35074539184570314\n",
            "EPOCH: 32\n",
            "Mean loss: 0.35132369995117185\n",
            "EPOCH: 33\n",
            "Mean loss: 0.3517112731933594\n",
            "EPOCH: 34\n",
            "Mean loss: 0.35189132690429686\n",
            "EPOCH: 35\n",
            "Mean loss: 0.35190658569335936\n",
            "EPOCH: 36\n",
            "Mean loss: 0.35185699462890624\n",
            "EPOCH: 37\n",
            "Mean loss: 0.3516365051269531\n",
            "EPOCH: 38\n",
            "Mean loss: 0.3514869689941406\n",
            "EPOCH: 39\n",
            "Mean loss: 0.35123367309570314\n",
            "EPOCH: 40\n",
            "Mean loss: 0.35092849731445314\n",
            "EPOCH: 41\n",
            "Mean loss: 0.35071868896484376\n",
            "EPOCH: 42\n",
            "Mean loss: 0.3505668640136719\n",
            "EPOCH: 43\n",
            "Mean loss: 0.35031280517578123\n",
            "EPOCH: 44\n",
            "Mean loss: 0.34995498657226565\n",
            "EPOCH: 45\n",
            "Mean loss: 0.34961395263671874\n",
            "EPOCH: 46\n",
            "Mean loss: 0.34929046630859373\n",
            "EPOCH: 47\n",
            "Mean loss: 0.3490272521972656\n",
            "EPOCH: 48\n",
            "Mean loss: 0.34865493774414064\n",
            "EPOCH: 49\n",
            "Mean loss: 0.3482948303222656\n",
            "EPOCH: 50\n",
            "Mean loss: 0.3478523254394531\n",
            "EPOCH: 51\n",
            "Mean loss: 0.3473968505859375\n",
            "EPOCH: 52\n",
            "Mean loss: 0.3468925476074219\n",
            "EPOCH: 53\n",
            "Mean loss: 0.3462501525878906\n",
            "EPOCH: 54\n",
            "Mean loss: 0.34557418823242186\n",
            "EPOCH: 55\n",
            "Mean loss: 0.344927978515625\n",
            "EPOCH: 56\n",
            "Mean loss: 0.34425201416015627\n",
            "EPOCH: 57\n",
            "Mean loss: 0.34362106323242186\n",
            "EPOCH: 58\n",
            "Mean loss: 0.34299697875976565\n",
            "EPOCH: 59\n",
            "Mean loss: 0.34238128662109374\n",
            "EPOCH: 60\n",
            "Mean loss: 0.3417572021484375\n",
            "EPOCH: 61\n",
            "Mean loss: 0.3410064697265625\n",
            "EPOCH: 62\n",
            "Mean loss: 0.34026947021484377\n",
            "EPOCH: 63\n",
            "Mean loss: 0.33939132690429685\n",
            "EPOCH: 64\n",
            "Mean loss: 0.3385284423828125\n",
            "EPOCH: 65\n",
            "Mean loss: 0.3375740051269531\n",
            "EPOCH: 66\n",
            "Mean loss: 0.3365791320800781\n",
            "EPOCH: 67\n",
            "Mean loss: 0.33577728271484375\n",
            "EPOCH: 68\n",
            "Mean loss: 0.33494415283203127\n",
            "EPOCH: 69\n",
            "Mean loss: 0.3341804504394531\n",
            "EPOCH: 70\n",
            "Mean loss: 0.33336868286132815\n",
            "EPOCH: 71\n",
            "Mean loss: 0.33260498046875\n",
            "EPOCH: 72\n",
            "Mean loss: 0.3318138122558594\n",
            "EPOCH: 73\n",
            "Mean loss: 0.33106155395507814\n",
            "EPOCH: 74\n",
            "Mean loss: 0.33043060302734373\n",
            "EPOCH: 75\n",
            "Mean loss: 0.32986297607421877\n",
            "EPOCH: 76\n",
            "Mean loss: 0.32936630249023435\n",
            "EPOCH: 77\n",
            "Mean loss: 0.32895050048828123\n",
            "EPOCH: 78\n",
            "Mean loss: 0.328704833984375\n",
            "EPOCH: 79\n",
            "Mean loss: 0.3283988952636719\n",
            "EPOCH: 80\n",
            "Mean loss: 0.32831802368164065\n",
            "EPOCH: 81\n",
            "Mean loss: 0.32841644287109373\n",
            "EPOCH: 82\n",
            "Mean loss: 0.32846221923828123\n",
            "EPOCH: 83\n",
            "Mean loss: 0.3286460876464844\n",
            "EPOCH: 84\n",
            "Mean loss: 0.32908172607421876\n",
            "EPOCH: 85\n",
            "Mean loss: 0.32995147705078126\n",
            "EPOCH: 86\n",
            "Mean loss: 0.33103408813476565\n",
            "EPOCH: 87\n",
            "Mean loss: 0.3322105407714844\n",
            "EPOCH: 88\n",
            "Mean loss: 0.3332489013671875\n",
            "EPOCH: 89\n",
            "Mean loss: 0.33416900634765623\n",
            "EPOCH: 90\n",
            "Mean loss: 0.33518829345703127\n",
            "EPOCH: 91\n",
            "Mean loss: 0.3362335205078125\n",
            "EPOCH: 92\n",
            "Mean loss: 0.3373626708984375\n",
            "EPOCH: 93\n",
            "Mean loss: 0.3384513854980469\n",
            "EPOCH: 94\n",
            "Mean loss: 0.33956756591796877\n",
            "EPOCH: 95\n",
            "Mean loss: 0.3408050537109375\n",
            "EPOCH: 96\n",
            "Mean loss: 0.3419097900390625\n",
            "EPOCH: 97\n",
            "Mean loss: 0.34290237426757814\n",
            "EPOCH: 98\n",
            "Mean loss: 0.3440895080566406\n",
            "EPOCH: 99\n",
            "Mean loss: 0.34512939453125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_losses = [mean(x) for x in losses]\n",
        "mean_losses\n",
        "\n",
        "plt.plot(list(range(len(mean_losses))), mean_losses)\n",
        "plt.title('Mean epoch loss')\n",
        "plt.xlabel('Epoch Number')\n",
        "plt.ylabel('Loss')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UKqIxUgAOTzp",
        "outputId": "9ff31bf2-532e-48e1-d3be-22d46afaa8cd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## We can compare our tuned model to the original model"
      ],
      "metadata": {
        "id": "TuDlIiRjmitT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load up the model with default weights\n",
        "sam_model_orig = sam_model_registry[model_type](checkpoint=checkpoint)\n",
        "sam_model_orig.to(device);"
      ],
      "metadata": {
        "id": "J9fZiPoIKXYW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up predictors for both tuned and original models\n",
        "from segment_anything import sam_model_registry, SamPredictor\n",
        "predictor_tuned = SamPredictor(sam_model)\n",
        "predictor_original = SamPredictor(sam_model_orig)"
      ],
      "metadata": {
        "id": "3dIKKKHOn_7R"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The model has not seen keys[21] (or keys[20]) since we only trained on keys[:20]\n",
        "k = keys[30]\n",
        "image = cv2.imread(f'datasetPng/scans/scans/{k}.png')\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "predictor_tuned.set_image(image)\n",
        "predictor_original.set_image(image)\n",
        "\n",
        "input_bbox = np.array(bbox_coords[k])\n",
        "\n",
        "masks_tuned, _, _ = predictor_tuned.predict(\n",
        "    point_coords=None,\n",
        "    box=input_bbox,\n",
        "    multimask_output=False,\n",
        ")\n",
        "\n",
        "masks_orig, _, _ = predictor_original.predict(\n",
        "    point_coords=None,\n",
        "    box=input_bbox,\n",
        "    multimask_output=False,\n",
        ")"
      ],
      "metadata": {
        "id": "nhNHx-6kpEWu"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see here that the tuned model is starting to ignore the whitespace between the words, which is what the ground truths show. With further training, more data and further hyperparameter tuning we will be able to improve this result.\n",
        "\n",
        "If the image does not render due to size limitations, you can view it here https://drive.google.com/file/d/1ip5aryaxcp8JcEaZubL76oOM6srVzKsc/view?usp=sharing ."
      ],
      "metadata": {
        "id": "Df2oxBaxxXrt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "_, axs = plt.subplots(1, 2, figsize=(25, 25))\n",
        "\n",
        "\n",
        "axs[0].imshow(image)\n",
        "show_mask(masks_tuned, axs[0])\n",
        "show_box(input_bbox, axs[0])\n",
        "axs[0].set_title('Mask with Tuned Model', fontsize=26)\n",
        "axs[0].axis('off')\n",
        "\n",
        "\n",
        "axs[1].imshow(image)\n",
        "show_mask(masks_orig, axs[1])\n",
        "show_box(input_bbox, axs[1])\n",
        "axs[1].set_title('Mask with Untuned Model', fontsize=26)\n",
        "axs[1].axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "sH6NorejpTii",
        "outputId": "8ac09e17-acf7-49fc-8634-d32c2754ff1e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2500 with 2 Axes>"
            ],
            "image/png": 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