MABe_2022_Ant_Beetle_Video_Unsupervised_Baseline_shared.ipynb

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      "source": [
        "!pip install torch torchvision simclr\n",
        "!pip install -U aicrowd-cli\n",
        "%load_ext aicrowd.magic"
      ]
    },
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      "metadata": {
        "id": "9TkEMV3g3_XV"
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      "source": [
        "%aicrowd login"
      ]
    },
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      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as T\n",
        "\n",
        "from simclr import SimCLR\n",
        "from simclr.modules import NT_Xent\n",
        "from simclr.modules import LARS"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "CIuB4eRvY38C"
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      "source": [
        "aicrowd_challenge_name = \"mabe-2022-ant-beetles-video-data\"\n",
        "if not os.path.exists('data'):\n",
        "    os.mkdir('data')\n",
        "\n",
        "datafolder = 'data/'\n",
        "\n",
        "## If data is already downloaded and stored on google drive, skip the download and point to the prepared directory\n",
        "# datafolder = '/content/drive/MyDrive/mabe-2022-ant-beetle/data/'\n",
        "\n",
        "video_folder = f'{datafolder}video_clips/'"
      ]
    },
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              "sample_submission.npy:   0%|          | 0.00/171M [00:00<?, ?B/s]"
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              "submission_videos_resized_224.zip:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
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      "source": [
        "## The download might take a while, recommend to move to Google Drive if you want to run multiple times.\n",
        "%aicrowd ds dl -c {aicrowd_challenge_name} -o data *.npy* # Download all files\n",
        "# We'll download the 224x224 videos since they're fast on the dataloader, but you can use the full sized videos if you want\n",
        "%aicrowd ds dl -c {aicrowd_challenge_name} -o data *resized_224* # Download all file\n",
        "# %aicrowd ds dl -c {aicrowd_challenge_name} -o data *videos.zip* # Download the 512x512 videos"
      ]
    },
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      "execution_count": null,
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        "id": "iabZhE1KZiaT"
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      "source": [
        "!unzip -q data/submission_videos_resized_224.zip  -d {video_folder}\n",
        "!unzip -q data/userTrain_videos_resized_224.zip -d {video_folder}\n",
        "\n",
        "## Careful when running the below commands - For copying to Google Drive\n",
        "# !rm data/submission_videos.zip data/userTrain_videos.zip\n",
        "# !cp -r data/ '/content/drive/MyDrive/mabe-2022-ant-beetle/data/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEFtmxXkbisj"
      },
      "source": [
        "# Train Unsupervised Baseline - SIMCLR 🏋️\n",
        "\n",
        "- We use Contrastive learning for the baseline for the MABe video datasets. The code uses SIMCLR (A Simple Framework for Contrastive Learning of Visual Representations) - https://arxiv.org/abs/2002.05709 - A popular and \"simple\" contrastive learning algorithm.\n",
        "\n",
        "- Some changes are made to SIMCLR use some specific ideas about the dataset. Namely frame stacking and cropping around the animal subjects.\n",
        "\n",
        "- We use a ResNet50 model with the pytorch simclr package (unofficial) https://github.com/sthalles/SimCLR to do unsupervised learning on the video data.\n",
        "\n",
        "- We also stack past and future frames with frame skipping to incorporate temporal information into each of the embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YnE_Ej7Xx89R"
      },
      "outputs": [],
      "source": [
        "class AntBeetleVideoDataset(torch.utils.data.Dataset):\n",
        "    \"\"\"\n",
        "    Reads frames from video files\n",
        "    \"\"\"\n",
        "    def __init__(self,\n",
        "                 datafolder,\n",
        "                 frame_number_map,\n",
        "                 keypoints,\n",
        "                 frame_skip,\n",
        "                 num_prev_frames,\n",
        "                 num_next_frames,\n",
        "                 frame_size=(224, 224),\n",
        "                 transform=None):\n",
        "        \"\"\"\n",
        "        Initializing the dataset with images and labels\n",
        "        \"\"\"\n",
        "        self.datafolder = datafolder\n",
        "        self.transform = transform\n",
        "        self.frame_number_map = frame_number_map\n",
        "        self.num_prev_frames = num_prev_frames\n",
        "        self.num_next_frames = num_next_frames\n",
        "        self.frame_skip = frame_skip\n",
        "        self.frame_size = frame_size\n",
        "        self.keypoints = keypoints\n",
        "\n",
        "        self._setup_frame_map()\n",
        "\n",
        "    def set_transform(self, transform):\n",
        "        self.transform = transform\n",
        "\n",
        "    def _setup_frame_map(self):\n",
        "        self._video_names = np.array(list(self.frame_number_map.keys()))\n",
        "        # IMPORTANT: the frame number map should be sorted for self.get_video_name to work\n",
        "        frame_nums = np.array([self.frame_number_map[k] for k in self._video_names])\n",
        "        self._frame_numbers = frame_nums[:, 0] - 1 # start values\n",
        "        assert np.all(np.diff(self._frame_numbers) > 0), \"Frame number map is not sorted\"\n",
        "\n",
        "        self.length = frame_nums[-1, 1] # last value is the total number of frames\n",
        "\n",
        "    def get_frame_info(self, global_index):\n",
        "        \"\"\" Returns corresponding video name and frame number\"\"\"\n",
        "        video_idx = np.searchsorted(self._frame_numbers, global_index) - 1\n",
        "        frame_index = global_index - (self._frame_numbers[video_idx] + 1)\n",
        "        return self._video_names[video_idx], frame_index\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.length\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        video_name, frame_index = self.get_frame_info(idx)\n",
        "\n",
        "        video_path = os.path.join(self.datafolder, video_name + '.avi')\n",
        "\n",
        "        nf = self.num_next_frames + self.num_prev_frames + 1\n",
        "        frames_array = np.zeros((*self.frame_size, nf), dtype=np.float32)\n",
        "\n",
        "        if not os.path.exists(video_path):\n",
        "#             raise FileNotFoundError(video_path)\n",
        "            if self.transform is not None:\n",
        "                frames_array = self.transform(frames_array)\n",
        "            return { \"idx\": idx,\n",
        "                     \"image\": frames_array,\n",
        "                    }\n",
        "\n",
        "\n",
        "        cap = cv2.VideoCapture(video_path)\n",
        "        num_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "        for arridx, fnum in enumerate(range(frame_index - self.num_prev_frames * self.frame_skip,\n",
        "                                            frame_index + self.num_next_frames * self.frame_skip + 1,\n",
        "                                            self.frame_skip + 1)):\n",
        "            if fnum < 0 or fnum >= num_video_frames:\n",
        "                continue\n",
        "            cap.set(cv2.CAP_PROP_POS_FRAMES, fnum)\n",
        "            success, frame = cap.read()\n",
        "            # print(fnum, frame_index, success)\n",
        "            if success:\n",
        "                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
        "                frames_array[:, :, arridx] = frame\n",
        "\n",
        "        if video_name in self.keypoints['sequences']:\n",
        "            bbox = self.keypoints['sequences'][video_name]['bbox']\n",
        "            if bbox.shape[0] > frame_index:\n",
        "                bbox = bbox[frame_index]\n",
        "                frames_array = frames_array[bbox[0]:bbox[2], bbox[1]:bbox[3]] # Crop the image so random crop is more useful\n",
        "\n",
        "        if self.transform is not None:\n",
        "            frames_array = self.transform(frames_array)\n",
        "\n",
        "        return { \"idx\": idx,\n",
        "                 \"image\": frames_array,\n",
        "               }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o3AxgSvpkMI"
      },
      "source": [
        "## Utilites - Optimizer, Transforms and Augmentations 🔧"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rgK4bnGOgQgw"
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      "outputs": [],
      "source": [
        "def load_optimizer(optimizer, epochs, weight_decay, batch_size, model):\n",
        "\n",
        "    scheduler = None\n",
        "    if optimizer == \"Adam\":\n",
        "        optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)  # TODO: LARS\n",
        "    elif optimizer == \"LARS\":\n",
        "        # optimized using LARS with linear learning rate scaling\n",
        "        # (i.e. LearningRate = 0.3 × BatchSize/256) and weight decay of 10−6.\n",
        "        learning_rate = 0.3 * batch_size / 256\n",
        "        optimizer = LARS(\n",
        "            model.parameters(),\n",
        "            lr=learning_rate,\n",
        "            weight_decay=weight_decay,\n",
        "            exclude_from_weight_decay=[\"batch_normalization\", \"bias\"],\n",
        "        )\n",
        "\n",
        "        # \"decay the learning rate with the cosine decay schedule without restarts\"\n",
        "        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
        "            optimizer, epochs, eta_min=0, last_epoch=-1\n",
        "        )\n",
        "    else:\n",
        "        raise NotImplementedError\n",
        "\n",
        "    return optimizer, scheduler\n",
        "\n",
        "def save_model(epoch, model_path, model, optimizer):\n",
        "    out = os.path.join(model_path, \"checkpoint_{}.tar\".format(epoch))\n",
        "\n",
        "    # To save a DataParallel model generically, save the model.module.state_dict().\n",
        "    # This way, you have the flexibility to load the model any way you want to any device you want.\n",
        "    if isinstance(model, torch.nn.DataParallel):\n",
        "        torch.save(model.module.state_dict(), out)\n",
        "    else:\n",
        "        torch.save(model.state_dict(), out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rHEdINVQgXOY"
      },
      "outputs": [],
      "source": [
        "class TransformsSimCLR:\n",
        "    def __init__(self, size, pretrained=True, n_channel=3, validation=False) -> None:\n",
        "        self.train_transforms = T.Compose([\n",
        "            T.ToTensor(),\n",
        "            T.RandomResizedCrop(size=size, scale=(0.25, 1.0)),\n",
        "            T.RandomHorizontalFlip(),\n",
        "            T.RandomVerticalFlip(),\n",
        "            # Taking the means of the normal distributions of the 3 channels\n",
        "            # since we are moving to grayscale\n",
        "            T.Normalize(mean=np.mean([0.485, 0.456, 0.406]).repeat(n_channel),\n",
        "                        std=np.sqrt(\n",
        "                            (np.array([0.229, 0.224, 0.225])**2).sum()/9).repeat(n_channel)\n",
        "                        ) if pretrained is True else T.Lambda(lambda x: x)\n",
        "        ])\n",
        "\n",
        "        self.validation_transforms = T.Compose([\n",
        "            T.ToTensor(),\n",
        "            T.Resize(size=size),\n",
        "            # Taking the means of the normal distributions of the 3 channels\n",
        "            # since we are moving to grayscale\n",
        "            T.Normalize(mean=np.mean([0.485, 0.456, 0.406]).repeat(n_channel),\n",
        "                        std=np.sqrt(\n",
        "                            (np.array([0.229, 0.224, 0.225])**2).sum()/9).repeat(n_channel)\n",
        "                        ) if pretrained is True else T.Lambda(lambda x: x)\n",
        "        ])\n",
        "\n",
        "        self.validation = validation\n",
        "\n",
        "    def __call__(self, x):\n",
        "        if not self.validation:\n",
        "            return self.train_transforms(x), self.train_transforms(x)\n",
        "        else:\n",
        "            return self.validation_transforms(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxCw8YNDpyb3"
      },
      "source": [
        "## Bounding box creation 📦\n",
        "\n",
        "Since most of the frame is empty, it is important that the ant beetle are cropped correctly when doing SimCLR augments. We use the keypoints to create rough bounding box coordinates around them.\n",
        "\n",
        "Note that these bounding boxes are made in a simple fixed pixel size cropping mechanism, feel free to change the bounding box generation system."
      ]
    },
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      "source": [
        "######## Prepare bounding boxes from keypoints ##########\n",
        "\n",
        "# Preparing some bounding box information to be used for cropping frames during training\n",
        "keypoints = np.load(os.path.join(datafolder, 'submission_keypoints.npy'), allow_pickle=True).item()\n",
        "\n",
        "for sk in tqdm(keypoints['sequences'].keys()):\n",
        "    kp = keypoints['sequences'][sk]['keypoints']\n",
        "    bboxes = []\n",
        "    for frame_idx in range(len(kp)):\n",
        "        allcoords = np.int32(kp[frame_idx].reshape(-1, 2) * 224)\n",
        "        padbbox = 50\n",
        "        crop_size = 224\n",
        "        minvals = max(np.min(allcoords[:, 0]) - padbbox, 0), max(np.min(allcoords[:, 1]) - padbbox, 0)\n",
        "        maxvals = min(np.max(allcoords[:, 0]) + padbbox, crop_size), min(np.max(allcoords[:, 1]) + padbbox, crop_size)\n",
        "        bbox = (*minvals, *maxvals)\n",
        "        bboxes.append(list(bbox))\n",
        "    keypoints['sequences'][sk]['bbox'] = np.array(bboxes)\n",
        "\n",
        "# Can save it you want and load later\n",
        "# keypoints = np.save(os.path.join(datafolder, 'submission_keypoints_bbox.npy'), keypoints)\n",
        "\n",
        "# keypoints = np.load(os.path.join(datafolder, 'submission_keypoints_bbox.npy'), allow_pickle=True).item()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0woljLXUqKvW"
      },
      "source": [
        "## Training ☑️\n",
        "\n",
        "Below are hyperparamers you can play around with. The runs are pretty slow, so you can reduce the epochs and steps per epochs to find the parameters you want to use.\n",
        "\n",
        "Note that we do not go over the entire dataset for each \"epoch\", because the whole dataset is huge.\n",
        "\n",
        "This code will only use the submission videos for unsupervised training, but you can change it to use all the videos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vX6o-dAfgbSn"
      },
      "outputs": [],
      "source": [
        "################### CONFIG #########################\n",
        "IS_PRETRAINED = True\n",
        "batch_size = 32\n",
        "epochs = 20\n",
        "\n",
        "# Stack frames with frame skip from the video sequences\n",
        "LEFT_WINDOW = 3\n",
        "RIGHT_WINDOW = 3\n",
        "IMG_SIZE = 224\n",
        "FRAME_SKIP = 1                                                                                                      # default was 5\n",
        "n_channel = LEFT_WINDOW + RIGHT_WINDOW + 1\n",
        "\n",
        "# Check batch size that fits when changing this\n",
        "embedding_size = 128\n",
        "\n",
        "# Full Dataset is huge, he're we limiting to steps per epoch\n",
        "steps_per_epoch = 100                                                                                              # default 1000 reduced to 100\n",
        "\n",
        "videos_folder = os.path.join(datafolder, 'video_clips') # TODO: Change this to combined folder\n",
        "frame_number_map = np.load(os.path.join(datafolder, 'frame_number_map.npy'), allow_pickle=True).item()\n",
        "\n",
        "checkpoint_folder = \"antbeetle_checkpoints/\" # Can change this to a Google drive folder\n",
        "if not os.path.exists(checkpoint_folder):\n",
        "    os.mkdir(checkpoint_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GE31DhhOhiwJ"
      },
      "outputs": [],
      "source": [
        "train_dataset = AntBeetleVideoDataset(datafolder=videos_folder,\n",
        "                      frame_number_map=frame_number_map,\n",
        "                      keypoints=keypoints,\n",
        "                      frame_skip=FRAME_SKIP,\n",
        "                      num_prev_frames=LEFT_WINDOW,\n",
        "                      num_next_frames=RIGHT_WINDOW,\n",
        "                      frame_size=(224, 224),\n",
        "                      transform=TransformsSimCLR(size=IMG_SIZE,\n",
        "                                                 pretrained=IS_PRETRAINED,\n",
        "                                                 n_channel=n_channel))\n",
        "\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True,\n",
        "    drop_last=True,\n",
        "    pin_memory=True,\n",
        "    num_workers=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X0dqKZJuisIN"
      },
      "outputs": [],
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "##################### MODEL ############################\n",
        "def get_simclr_model():\n",
        "    resnet_encoder = torchvision.models.resnet101(pretrained=IS_PRETRAINED)\n",
        "\n",
        "    ## Experimental setup for multiplying the grayscale channel\n",
        "    ## https://stackoverflow.com/a/54777347\n",
        "    weight = resnet_encoder.conv1.weight.clone()\n",
        "    resnet_encoder.conv1 = torch.nn.Conv2d(n_channel, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
        "    # normalize back by n_channels after tiling\n",
        "    resnet_encoder.conv1.weight.data = weight.sum(dim=1, keepdim=True).tile(1, n_channel, 1, 1)/n_channel\n",
        "\n",
        "\n",
        "    n_features = resnet_encoder.fc.in_features\n",
        "    model = SimCLR(resnet_encoder, embedding_size, n_features)\n",
        "    model = model.to(device)\n",
        "\n",
        "    return model\n",
        "\n",
        "model = get_simclr_model()\n",
        "\n",
        "##################### UTILS ############################\n",
        "\n",
        "optimizer_type = 'Adam'\n",
        "weight_decay = 1e-6\n",
        "\n",
        "optimizer, scheduler = load_optimizer(optimizer_type, epochs, weight_decay, batch_size, model)\n",
        "\n",
        "world_size = 1\n",
        "temperature = 0.5\n",
        "criterion = NT_Xent(batch_size, temperature, world_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ylWAIT6cjVE7"
      },
      "outputs": [],
      "source": [
        "# Basic Training loop\n",
        "def train(epoch, train_loader, model, criterion, optimizer):\n",
        "    loss_epoch = 0\n",
        "\n",
        "    # tqdm_iter = tqdm(train_loader, total=len(train_loader)) # Total train loader is huge, he're we limiting to steps per epoch\n",
        "    tqdm_iter = tqdm(train_loader, total=steps_per_epoch)\n",
        "    tqdm_iter.set_description(f\"Epoch {epoch}\")\n",
        "\n",
        "    for step, batch in enumerate(tqdm_iter):\n",
        "        optimizer.zero_grad()\n",
        "        x_i = batch['image'][0].cuda(non_blocking=True)\n",
        "        x_j = batch['image'][1].cuda(non_blocking=True)\n",
        "\n",
        "        # positive pair, with encoding\n",
        "        h_i, h_j, z_i, z_j = model(x_i, x_j)\n",
        "        loss = criterion(z_i, z_j)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        tqdm_iter.set_postfix(iter_loss=loss.item())\n",
        "        loss_epoch += loss.item()\n",
        "        if step >= steps_per_epoch:\n",
        "            break\n",
        "\n",
        "    return loss_epoch"
      ]
    },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe62cb51050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1358, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1341, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/100 [00:01<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c337d9c983a146f4a462ac80bb4c62f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bfb1fa1fbb904b459b56850274a65c0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1e6e0da0601426d987a89194030940b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6935ae64796c49edb0a6800459161057"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:01<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dd79d1fdd554aa184921407abfd4ff2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/100 [00:01<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e47c71886dbe4eb79582ba3af4340d0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:01<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6ba67f92e324d048092aa84621b3b5f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Baseline submission on the leaderboard is trained with 100 epochs, you can train according to your needs\n",
        "for epoch in range(epochs):\n",
        "    lr = optimizer.param_groups[0]['lr']\n",
        "    loss_epoch = train(epoch, train_loader, model, criterion, optimizer)\n",
        "\n",
        "    if scheduler:\n",
        "        scheduler.step()\n",
        "\n",
        "    if (epoch % 3) == 0:\n",
        "        save_model(epoch, checkpoint_folder, model, optimizer)\n",
        "\n",
        "save_model(epochs, checkpoint_folder, model, optimizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SKIrszpfnsbG"
      },
      "outputs": [],
      "source": [
        "# Cleanup RAM\n",
        "del model, optimizer\n",
        "del train_loader, train_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dx_F-k3xnsbG"
      },
      "source": [
        "# Predict Embeddings 🔮\n",
        "\n",
        "Here we'll predict the outputs from the frames, this may take a long time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sk8hFWoNnsbG"
      },
      "outputs": [],
      "source": [
        "# Load latest model\n",
        "model = get_simclr_model()\n",
        "checkpoint_path = os.path.join(checkpoint_folder, 'checkpoint_6.tar')\n",
        "model.load_state_dict(torch.load(checkpoint_path))\n",
        "model = model.to(device)\n",
        "model.eval();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2cIZmY6ansbH"
      },
      "outputs": [],
      "source": [
        "prediction_dataset = AntBeetleVideoDataset(datafolder=videos_folder,\n",
        "                                          frame_number_map=frame_number_map,\n",
        "                                          keypoints=keypoints,\n",
        "                                          frame_skip=FRAME_SKIP,\n",
        "                                          num_prev_frames=LEFT_WINDOW,\n",
        "                                          num_next_frames=RIGHT_WINDOW,\n",
        "                                          frame_size=(224, 224),\n",
        "                                          transform=TransformsSimCLR(size=IMG_SIZE,\n",
        "                                                                     pretrained=IS_PRETRAINED,\n",
        "                                                                     n_channel=n_channel,\n",
        "                                                                     validation=True))\n",
        "\n",
        "prediction_batch_size=128\n",
        "\n",
        "prediction_loader = torch.utils.data.DataLoader(\n",
        "    prediction_dataset,\n",
        "    batch_size=prediction_batch_size,\n",
        "    shuffle=False,\n",
        "    drop_last=False,\n",
        "    pin_memory=True,\n",
        "    num_workers=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5QHmzM3xnsbH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620,
          "referenced_widgets": [
            "80659156a7fa45dca4646389baea4fd6",
            "58094e01540b4dffba1f364eede6c911",
            "74aba1ae21f64ff8851ba6d971bb02d9",
            "39858b0cab3c479aa636104d9e1e11f9",
            "d781ac5d78fc4133931ca0e496ac02f9",
            "13119ca5a6af46708ec28c2e8e26d9a2",
            "ac1b3d273db545bdb4a7bf96d1b565a2",
            "46796fbd71494ae3a7ad259cb06fd2fc",
            "7cc6a1f0400f48699bf59dd8debde841",
            "26a55ee557af4a45ba32bcef8e31575a",
            "4aa0297276d6424cb97c0bf61b74b378"
          ]
        },
        "outputId": "912207dc-4b99-468f-f74a-d4caac9ac93c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/66734 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80659156a7fa45dca4646389baea4fd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-1b12ef243456>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# This may take quite long, since predicting on all frames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1195\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1196\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    528\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1222\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1224\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1248\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1249\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1250\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1251\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    455\u001b[0m             \u001b[0;31m# instantiate since we don't know how to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n    return self.collate_fn(data)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 157, in default_collate\n    return elem_type({key: default_collate([d[key] for d in batch]) for key in elem})\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 157, in <dictcomp>\n    return elem_type({key: default_collate([d[key] for d in batch]) for key in elem})\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\", line 138, in default_collate\n    return torch.stack(batch, 0, out=out)\nRuntimeError: stack expects each tensor to be equal size, but got [7, 224, 232] at entry 0 and [7, 224, 235] at entry 3\n"
          ]
        }
      ],
      "source": [
        "sample_submission = np.load(datafolder + 'sample_submission.npy')\n",
        "submission = np.empty((sample_submission.shape[0], embedding_size), dtype=np.float32)\n",
        "idx = 0\n",
        "# This may take quite long, since predicting on all frames\n",
        "for data in tqdm(prediction_loader, total=len(prediction_loader)):\n",
        "    with torch.no_grad():\n",
        "        images = data['image'].to(device)\n",
        "        output = model.projector(model.encoder(images))\n",
        "        output = output.cpu().numpy()\n",
        "        submission[idx:idx+len(output)] = output\n",
        "        idx += len(output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hu4mHqEansbH"
      },
      "source": [
        "# Submission 🚀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g82n2FY0nsbH",
        "outputId": "eccc9387-95e5-4b37-8b04-fe2d92fcf4e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding shape: (8541900, 128)\n"
          ]
        }
      ],
      "source": [
        "print(\"Embedding shape:\", submission.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGAtTpYtnsbH"
      },
      "source": [
        "## Validate the submission ✅\n",
        "The submssion should follow these constraints:\n",
        "\n",
        "- It should be a numpy array\n",
        "- Embeddings is an 2D numpy array of dtype float32\n",
        "- The embedding size should't exceed 128\n",
        "- The frame number map matches the clip lengths\n",
        "- You can use the helper function below to check these"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zyg6fFjQnsbI"
      },
      "outputs": [],
      "source": [
        "def validate_submission(submission, frame_number_map):\n",
        "    if not isinstance(submission, np.ndarray):\n",
        "        print(\"Embeddings should be a numpy array\")\n",
        "        return False\n",
        "    elif not len(submission.shape) == 2:\n",
        "        print(\"Embeddings should be 2D array\")\n",
        "        return False\n",
        "    elif not submission.shape[1] <= 128:\n",
        "        print(\"Embeddings too large, max allowed is 128\")\n",
        "        return False\n",
        "    elif not isinstance(submission[0, 0], np.float32):\n",
        "        print(f\"Embeddings are not float32\")\n",
        "        return False\n",
        "\n",
        "\n",
        "    total_clip_length = frame_number_map[list(frame_number_map.keys())[-1]][1]\n",
        "\n",
        "    if not len(submission) == total_clip_length:\n",
        "        print(f\"Emebddings length doesn't match submission clips total length\")\n",
        "        return False\n",
        "\n",
        "    if not np.isfinite(submission).all():\n",
        "        print(f\"Emebddings contains NaN or infinity\")\n",
        "        return False\n",
        "\n",
        "    print(\"All checks passed\")\n",
        "    return True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyzzCd99nsbI",
        "outputId": "be7e5679-2175-4e40-cbb6-a927f8f559f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All checks passed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "validate_submission(submission, frame_number_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z2aFL2-3nsbI"
      },
      "outputs": [],
      "source": [
        "np.save('submission_ant_simclr.npy', submission)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission_ant_simclr.npy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "2-y2n2p2iTA7",
        "outputId": "c167fe46-4b31-4570-8531-1eb65920f401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_5d5c6637-3119-4dde-b6d8-dff48bf1816c\", \"submission_ant_simclr.npy\", 4373452928)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LOm-Xv4unsbI"
      },
      "outputs": [],
      "source": [
        "## Uploads may take time, you can also rund aicrowd-cli on your local machines with he prepared submission file\n",
        "# %aicrowd submission create --description \"Ant-Beetle SimCLR Baseline\" -c {aicrowd_challenge_name} -f submission_ant_simclr.npy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tq0xbTBQSkMv"
      },
      "outputs": [],
      "source": []
    }
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