ITL_fastai_training.ipynb

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      "display_name": "Python 3",
      "language": "python",
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    "colab": {
      "name": "ITL fastai - training final",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1ydA9qXkUmK",
        "outputId": "730530ff-3a61-4e97-eb36-c4207589b8ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Oct 24 12:38:00 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.23.05    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HobAbLXiMI0H",
        "outputId": "8c4f3736-35d5-4eb2-8758-72440c56f105",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Run once per session\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "!pip install -q -U fastai"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 194kB 4.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 6.7MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWs7SXc2MI0O"
      },
      "source": [
        "# ITL fast.ai model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hf1lY_FMI0Q"
      },
      "source": [
        "from fastai.vision.all import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1GGI9RBMI0U",
        "outputId": "7f0b5318-09ab-4c2b-f097-51190c97cb3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "VERSION=16\n",
        "ITL_DS_NAME = f'itl_dataset_v{VERSION}.zip'\n",
        "ITL_DS_URL =  f'http://students.mimuw.edu.pl/~mt406390/machine_learning/' + ITL_DS_NAME\n",
        "!wget $ITL_DS_URL\n",
        "!unzip -qq $ITL_DS_NAME -d data/\n",
        "path = Path('data')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-24 12:39:16--  http://students.mimuw.edu.pl/~mt406390/machine_learning/itl_dataset_v16.zip\n",
            "Resolving students.mimuw.edu.pl (students.mimuw.edu.pl)... 193.0.96.129, 2001:6a0:5001:1::3\n",
            "Connecting to students.mimuw.edu.pl (students.mimuw.edu.pl)|193.0.96.129|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://students.mimuw.edu.pl/~mt406390/machine_learning/itl_dataset_v16.zip [following]\n",
            "--2020-10-24 12:39:16--  https://students.mimuw.edu.pl/~mt406390/machine_learning/itl_dataset_v16.zip\n",
            "Connecting to students.mimuw.edu.pl (students.mimuw.edu.pl)|193.0.96.129|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 640430604 (611M) [application/zip]\n",
            "Saving to: ‘itl_dataset_v16.zip’\n",
            "\n",
            "itl_dataset_v16.zip 100%[===================>] 610.76M  7.60MB/s    in 2m 29s  \n",
            "\n",
            "2020-10-24 12:41:45 (4.10 MB/s) - ‘itl_dataset_v16.zip’ saved [640430604/640430604]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKWezwyLMI0Y",
        "outputId": "9ad45ec7-6a27-47a7-82af-94e3ba43e7f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "path.ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#2) [Path('data/output_proper'),Path('data/annotations.json')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXEu4SWkMI0d",
        "outputId": "e5a6fa71-f34e-4863-f23e-a379a3fc34ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "with open(path / 'annotations.json') as f:\n",
        "    annots = json.load(f)\n",
        "print(len(annots))\n",
        "# FIXME (17 bboxes missing)\n",
        "annots = list(filter(lambda x: len(x['exprs']) > 0, annots))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pPlgsF6MI0g",
        "outputId": "b8d07d56-ad50-45b8-8e3c-1f295ed481ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "images = list(x['name'] for x in annots if len(x['exprs']) > 0)\n",
        "print(len(images))\n",
        "images[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100008\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "type": "string"
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            "text/plain": [
              "'eq0.png'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eawdh5vfvOXt"
      },
      "source": [
        "ops_cfg = {\n",
        "  \"leaf\": {\n",
        "    \"latex\": \"{0}\",\n",
        "    \"operands\": 0,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"add\": {\n",
        "    \"latex\": \"{0}+{1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"times\": {\n",
        "    \"latex\": \"{0} \\\\times {1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"star\": {\n",
        "    \"latex\": \"{0} \\\\star {1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"div\": {\n",
        "    \"latex\": \"{0} \\\\div {1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"otimes\": {\n",
        "    \"latex\": \"{0} \\\\otimes {1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"sub\": {\n",
        "    \"latex\": \"{0}-{1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"mul\": {\n",
        "    \"latex\": \"{0} \\\\cdot {1}\",\n",
        "    \"operands\": 2,\n",
        "    \"inline\": true\n",
        "  },\n",
        "  \"frac\": {\n",
        "    \"latex\": \"\\\\frac{{ {0} }}{{ {1} }}\",\n",
        "    \"operands\": 2\n",
        "  },\n",
        "  \"sqrt\": {\n",
        "    \"latex\": \"\\\\sqrt{{ {0} }}\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"int\": {\n",
        "    \"latex\": \"\\\\int \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"sum\": {\n",
        "    \"latex\": \"\\\\sum \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"pi\": {\n",
        "    \"latex\": \"\\\\prod \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"par\": {\n",
        "    \"latex\": \"\\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"fpar\": {\n",
        "    \"latex\": \"f \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"ffpar\": {\n",
        "    \"latex\": \"F \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  },\n",
        "  \"gpar\": {\n",
        "    \"latex\": \"G \\\\left( {0} \\\\right)\",\n",
        "    \"operands\": 1\n",
        "  }\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XnwBchlMI0m"
      },
      "source": [
        "Next, we want to be able to quickly look up a corresponding image to it's label. We'll use a dictionary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOOYWbhxMI0n"
      },
      "source": [
        "img2bbox = dict(zip(images, annots))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZjUaaI8MI0t"
      },
      "source": [
        "Great! Now let's build our `DataBlock`. We'll have two outputs, the bounding box itself and a label, with one input. For our `y`'s, we'll define some `getters`. The first of which will be `noop`, as we don't care about our initial image, and then we'll grab our label and bbox coordinates (we'll also define our `get_items`)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqMkzWBkMI0t"
      },
      "source": [
        "def bbox_to_fastai(box):\n",
        "    x, y, dx, dy = box\n",
        "    return [x, y, x + dx, y + dy]\n",
        "\n",
        "getters = [\n",
        "    lambda o: path/'output_proper'/o, lambda o: list(map(bbox_to_fastai, img2bbox[o]['exprs'])),\n",
        "    lambda o: len(img2bbox[o]['exprs']) * [img2bbox[o]['op']]\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlvhInImMI0w"
      },
      "source": [
        "For our transforms, we'll use some of the ones we defined earlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYOBW1HMMI0x"
      },
      "source": [
        "item_tfms = []\n",
        "batch_tfms = [Zoom(0.15, 1.3, pad_mode=PadMode.Border), Warp(0.03), Saturation(max_lighting=0.5),\n",
        "              Brightness(), Rotate()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YrjfDxuMI0z"
      },
      "source": [
        "Why do we need a custom `get_images`? Because we want our **images** that came back to us, not the entire folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diaZXpW4MI00"
      },
      "source": [
        "def get_train_imgs(noop):  return images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oKEQM7HMI03"
      },
      "source": [
        "We'll now make our `DataBlock`. We want to adjust `n_inp` as we expect two outputs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qD57DuQGMI03"
      },
      "source": [
        "pascal = DataBlock(blocks=(ImageBlock(cls=PILImageBW), BBoxBlock, BBoxLblBlock),\n",
        "                 splitter=RandomSplitter(),\n",
        "                 get_items=get_train_imgs, \n",
        "                 getters=getters,\n",
        "                 item_tfms=item_tfms,\n",
        "                 batch_tfms=batch_tfms,\n",
        "                 n_inp=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OadwEe45MI06"
      },
      "source": [
        "BS = 64\n",
        "dls = pascal.dataloaders(path/'train', bs=BS)\n",
        "dls.c = len(set(x['op'] for x in annots))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqEEJ0AkMI0_",
        "outputId": "fb5ace6e-1449-4a21-8063-de9c68a49a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        }
      },
      "source": [
        "dls.show_batch(max_n=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZB-fSbhqQsnh"
      },
      "source": [
        "# ITL model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpRX2rdUVtNK"
      },
      "source": [
        "POINTS_PER_BBOX = 4\n",
        "N_BBOXES = 2\n",
        "BBOXES = N_BBOXES * POINTS_PER_BBOX\n",
        "CATS = dls.c\n",
        "OUT = CATS + BBOXES"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTeL1CgLBiGx"
      },
      "source": [
        "head = nn.Sequential(\n",
        "    nn.Conv2d(512, 128, 3, 2),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.Conv2d(128, OUT, 3, 1),\n",
        "    # nn.BatchNorm2d(OUT),\n",
        "    nn.Flatten()\n",
        ")\n",
        "\n",
        "bigger_shallow_head = nn.Sequential(\n",
        "    nn.Conv2d(2048, 128, 3, 2),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.Conv2d(128, OUT, 3, 1),\n",
        "    # nn.BatchNorm2d(OUT),\n",
        "    nn.Flatten()\n",
        ")\n",
        "\n",
        "bigger_head = nn.Sequential(\n",
        "    nn.Conv2d(512, 256, 3, 1),\n",
        "    nn.BatchNorm2d(256),\n",
        "    nn.Conv2d(256, 128, 3, 1),\n",
        "    nn.BatchNorm2d(128),\n",
        "    nn.Conv2d(128, OUT, 3, 1),\n",
        "    # nn.BatchNorm2d(OUT),\n",
        "    nn.Flatten()\n",
        ")\n",
        "\n",
        "fc = create_head(1024, OUT)\n",
        "\n",
        "cfg = cnn_config(n_in=1, custom_head=head)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lkvpmr5JJ9vP"
      },
      "source": [
        "from torchvision.ops import box_iou\n",
        "\n",
        "from pdb import set_trace\n",
        "\n",
        "def encode_boxes(boxes_points):\n",
        "    boxes_points = (boxes_points + 1) / 2\n",
        "    x1, y1, x2, y2 = boxes_points[..., 0], boxes_points[..., 1], boxes_points[..., 2], boxes_points[..., 3]\n",
        "    return torch.stack((x1, y1, torch.sqrt(x2 - x1), torch.sqrt(y2 - y1)), dim=-1)\n",
        "    \n",
        "def decode_boxes(boxes_lens):\n",
        "    x, y, dx, dy = boxes_lens[..., 0], boxes_lens[..., 1], torch.square(boxes_lens[..., 2]), torch.square(boxes_lens[..., 3])\n",
        "    boxes_points = torch.stack((x, y, x + dx, y + dy), dim=-1)\n",
        "    return 2 * boxes_points - 1\n",
        "\n",
        "class MseAndCrossEntropy(Module):\n",
        "    def forward(self, pred, target_bboxes, target_cat):\n",
        "        bboxes_preds = torch.sigmoid(pred[..., CATS:]).view((-1, N_BBOXES, POINTS_PER_BBOX))\n",
        "        mask = (target_cat > 0)\n",
        "\n",
        "        box_loss = F.mse_loss(bboxes_preds[mask], target_bboxes[mask])\n",
        "        ce_loss = F.cross_entropy(pred[..., :CATS], target_cat[..., 0] - 1)\n",
        "        return ce_loss + 50 * torch.sqrt(box_loss)\n",
        "\n",
        "def ops_accuracy(pred, target_bboxes, target_cat):\n",
        "    return accuracy(pred[..., :CATS], target_cat[..., 0] - 1)\n",
        "    \n",
        "def boxes_iou(pred, target_bboxes, target_cat):\n",
        "    bboxes_preds = torch.sigmoid(pred[..., CATS:]).view((-1, N_BBOXES, POINTS_PER_BBOX))\n",
        "    bboxes_decoded = decode_boxes(bboxes_preds)\n",
        "\n",
        "    mask = (target_cat > 0)\n",
        "    x, y = bboxes_decoded[mask], target_bboxes[mask]\n",
        "    ious = torch.diagonal(box_iou(x, y))\n",
        "    return ious.mean()\n",
        "\n",
        "\n",
        "@log_args\n",
        "class ITLLoss(BaseLoss):\n",
        "    def __init__(self, *args, axis=-1, **kwargs):\n",
        "        super().__init__(MseAndCrossEntropy, *args, axis=axis, **kwargs)\n",
        "\n",
        "    def __call__(self, inp, targ, labels, **kwargs):\n",
        "        targ_encoded = encode_boxes(targ)\n",
        "\n",
        "        return self.func.__call__(inp, targ_encoded, labels)\n",
        "\n",
        "    def decodes(self, x):\n",
        "        categories, bboxes = x[...,:CATS], x[...,CATS:]\n",
        "        bboxes = torch.sigmoid(bboxes)\n",
        "\n",
        "        # Scaling back\n",
        "        bboxes = decode_boxes(bboxes.view(-1, 2, 4))\n",
        "        \n",
        "\n",
        "        cats = (1 + categories.argmax(dim=-1)).unsqueeze(dim=-1)\n",
        "\n",
        "        cats_stacked = torch.cat((cats, torch.zeros(cats.shape, dtype=torch.int64)), axis=-1)\n",
        "        n = bboxes.shape[0]\n",
        "        for i in range(n):\n",
        "            current_cat = cats[i, 0]\n",
        "            cat_lbl = dls.vocab[current_cat]\n",
        "            if ops_cfg[cat_lbl]['operands'] == 2:\n",
        "                cats_stacked[i, 1] = cats_stacked[i, 0]\n",
        "            else:\n",
        "                bboxes[i, 1] = 0\n",
        "\n",
        "        return bboxes, TensorMultiCategory(cats_stacked)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uokx32ABlOkk",
        "outputId": "9525662b-1a6c-4424-b930-0052cb70fb12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "learn = cnn_learner(dls, xse_resnext18, loss_func=ITLLoss(), pretrained=False, config=cfg,\n",
        "                    metrics=[ops_accuracy, boxes_iou])\n",
        "# learn.fit_one_cycle(20, 0.001905)\n",
        "learn.fit_one_cycle(30, 0.00229)\n",
        "# learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='10' class='' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      33.33% [10/30 57:46<1:55:33]\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>ops_accuracy</th>\n",
              "      <th>boxes_iou</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.273460</td>\n",
              "      <td>3.699414</td>\n",
              "      <td>0.497475</td>\n",
              "      <td>0.561155</td>\n",
              "      <td>05:48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.465281</td>\n",
              "      <td>18.160482</td>\n",
              "      <td>0.284086</td>\n",
              "      <td>0.235983</td>\n",
              "      <td>05:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.487345</td>\n",
              "      <td>9.199334</td>\n",
              "      <td>0.227439</td>\n",
              "      <td>0.306596</td>\n",
              "      <td>05:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.953413</td>\n",
              "      <td>30.874979</td>\n",
              "      <td>0.169991</td>\n",
              "      <td>0.116048</td>\n",
              "      <td>05:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.711419</td>\n",
              "      <td>28.060890</td>\n",
              "      <td>0.079646</td>\n",
              "      <td>0.105072</td>\n",
              "      <td>05:46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.536054</td>\n",
              "      <td>63.803078</td>\n",
              "      <td>0.064197</td>\n",
              "      <td>0.076861</td>\n",
              "      <td>05:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.568643</td>\n",
              "      <td>32.537380</td>\n",
              "      <td>0.064747</td>\n",
              "      <td>0.151017</td>\n",
              "      <td>05:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.297380</td>\n",
              "      <td>44.292587</td>\n",
              "      <td>0.064197</td>\n",
              "      <td>0.096453</td>\n",
              "      <td>05:46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.119673</td>\n",
              "      <td>26.348404</td>\n",
              "      <td>0.080396</td>\n",
              "      <td>0.146919</td>\n",
              "      <td>05:46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.060540</td>\n",
              "      <td>42.795303</td>\n",
              "      <td>0.064197</td>\n",
              "      <td>0.159642</td>\n",
              "      <td>05:51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='123' class='' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      9.84% [123/1250 00:29<04:29 1.0222]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUN4BldFYfZQ",
        "outputId": "dde90663-ece2-453b-99ff-fcf1d4674381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "learn.save('infer30_16', with_opt=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/infer30_16.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBAFPaq6CUNM",
        "outputId": "ebfe58e0-738d-468d-ef27-166a2b23ff13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.show_results(max_n=36)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1296x1296 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKU6ClxA28v1"
      },
      "source": [
        "class Predictor():\n",
        "    def __init__(self, learn: Learner):\n",
        "        self.learn = learn\n",
        "\n",
        "        self.labeler, self.point_scaler, self.to_tensor = learn.dls.valid.after_item\n",
        "        self.scale_pixels = learn.dls.valid.after_batch[0]\n",
        "\n",
        "        self.tfms = [self.labeler, self.point_scaler, self.to_tensor, self.scale_pixels]\n",
        "\n",
        "        self.loss = ITLLoss()\n",
        "    \n",
        "    def _transform_img(self, img: PILImageBW) -> TensorImageBW:\n",
        "        return self.scale_pixels(self.to_tensor(self.labeler(self.point_scaler(img))))[None]\n",
        "\n",
        "    def _decode_after_fastai(self, boxes, cats):\n",
        "        boxes = ((1 + boxes) / 2) * img.shape[1]\n",
        "        \n",
        "        # set_trace()\n",
        "        x1, y1, x2, y2 = (boxes[0, :, i] for i in range(POINTS_PER_BBOX))\n",
        "        boxes_coco = torch.stack((x1, y1, x2 - x1, y2 - y1), dim=-1)\n",
        "\n",
        "        cat = self.learn.dls.vocab[cats[0, 0]]\n",
        "        return boxes_coco, cat\n",
        "\n",
        "    def _decode_preds(self, x):\n",
        "        boxes, cats = self.loss.decodes(x)\n",
        "        return self._decode_after_fastai(boxes, cats)\n",
        "\n",
        "\n",
        "    def predict(self, img: PILImageBW):\n",
        "        with torch.no_grad():\n",
        "            preds = self.learn.model(to_device(self._transform_img(img), default_device()))\n",
        "\n",
        "        return self._decode_preds(preds.cpu())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a__KYhXLfDEX"
      },
      "source": [
        "import abc\n",
        "from dataclasses import dataclass\n",
        "from typing import List\n",
        "\n",
        "@dataclass\n",
        "class Box:\n",
        "    x: int\n",
        "    y: int\n",
        "    dx: int\n",
        "    dy: int\n",
        "\n",
        "@dataclass\n",
        "class Operator:\n",
        "    op_id: str\n",
        "\n",
        "@dataclass\n",
        "class Prediction:\n",
        "    op: Operator\n",
        "    bboxes: List[Box]\n",
        "\n",
        "class Model(abc.ABC):\n",
        "    @abc.abstractmethod\n",
        "    def predict(self, input) -> Prediction:\n",
        "        raise NotImplementedError\n",
        "\n",
        "class ModelExample(Model):\n",
        "    def predict(self, input) -> Prediction:\n",
        "        return 'add', [Box(25, 25, 17, 17), Box(34, 34, 17, 17)]\n",
        "\n",
        "class FastaiModelAdapter(Model):\n",
        "    def __init__(self, predictor: Predictor):\n",
        "        self._pred = predictor\n",
        "\n",
        "    def _box_from_tensor(self, box_tensor):\n",
        "        return Box(*map(lambda x: int(x.round().item()), box_tensor))\n",
        "\n",
        "    def _make_pred(self, boxes_tensor, cat):\n",
        "        boxes = [self._box_from_tensor(boxes_tensor[i])\n",
        "            for i in range(boxes_tensor.shape[0])]\n",
        "\n",
        "        return Prediction(\n",
        "            op=Operator(cat),\n",
        "            bboxes=boxes\n",
        "        )\n",
        "\n",
        "    def predict(self, input: PILImageBW) -> Prediction:\n",
        "        return self._make_pred(*self._pred.predict(input))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3F75fSl1F-_"
      },
      "source": [
        "pred = Predictor(learn)\n",
        "fastai_model = FastaiModelAdapter(pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrZ3I2660XNm"
      },
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "def resize_and_center_pred(full_img: PILImageBW, pred_bbox: Box) -> PILImageBW:\n",
        "    def crop_img(image, bbox):\n",
        "        proc = 0.03\n",
        "        x_margin, y_margin = int(proc * bbox.dx), int(proc * bbox.dy) \n",
        "        return image[bbox.y:bbox.y + bbox.dy + y_margin, bbox.x:bbox.x + bbox.dx + x_margin]\n",
        "\n",
        "    img_arr = np.array(full_img)\n",
        "    img_cropped = crop_img(img_arr, pred_bbox)\n",
        "\n",
        "    DIM, WHITE = 224, 255\n",
        "    res = np.full((DIM, DIM), WHITE, dtype=np.uint8)\n",
        "\n",
        "    y_shift = (DIM - img_cropped.shape[0]) // 2\n",
        "    x_shift = (DIM - img_cropped.shape[1]) // 2\n",
        "    \n",
        "    res[y_shift:y_shift + img_cropped.shape[0], x_shift:x_shift + img_cropped.shape[1]] = img_cropped\n",
        "\n",
        "    return PILImageBW.create(res)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5abFwAuswkI"
      },
      "source": [
        "class Inference:\n",
        "    def __init__(self, model: Model):\n",
        "        self.model = model\n",
        "        self.MAX_DEPTH = 8\n",
        "\n",
        "    def __call__(self, input: PILImageBW, depth = 0) -> str:\n",
        "        if depth > self.MAX_DEPTH:\n",
        "            raise ValueError('stack overflow - your model too bad bro')\n",
        "\n",
        "        pred = self.model.predict(input)\n",
        "\n",
        "        operands = ops_cfg[pred.op.op_id]['operands']\n",
        "        latex = ops_cfg[pred.op.op_id]['latex']\n",
        "\n",
        "        if operands == 0:\n",
        "            return latex\n",
        "        \n",
        "        inputs = [resize_and_center_pred(input, pred.bboxes[i]) for i in range(operands)]\n",
        "\n",
        "        return latex.format(*(self(inp, depth + 1) for inp in inputs))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2qsUihMw5M8"
      },
      "source": [
        "infer = Inference(fastai_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3wdtrPZF-8z",
        "outputId": "f0798391-c0c8-42ef-f00e-05433d22dde1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "# learn.dls.valid_ds.tls[0].tfms\n",
        "# img\n",
        "def img_from_path(path: Path) -> PILImageBW:\n",
        "    return PILImageBW.create(path)\n",
        "\n",
        "img = img_from_path(getters[0](images[50153]))\n",
        "img"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "PILImageBW mode=L size=224x224"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFBAtwyLyK8P",
        "outputId": "821f4202-e8ca-47c3-c121-6428d56dee98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "infer(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\\\sum \\\\left( {0} \\\\right) \\\\times {0} \\\\div {0}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VK5EhWFthwS",
        "outputId": "6b7e0928-a2d5-487d-e4a9-ca4fbed8ab9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_N1xGPdFusqV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}