non-contrastive-based-method-for-image-similarity.ipynb

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   },
   "source": [
    "# Imports"
   ]
  },
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   "source": [
    "import copy\n",
    "import random\n",
    "from functools import wraps\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import torch.distributed as dist\n",
    "import torch.nn.init as init\n",
    "\n",
    "from torchvision import transforms as T"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# Helper Functions"
   ]
  },
  {
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   "source": [
    "# helper functions\n",
    "\n",
    "def default(val, def_val):\n",
    "    return def_val if val is None else val\n",
    "\n",
    "def flatten(t):\n",
    "    return t.reshape(t.shape[0], -1)\n",
    "\n",
    "def singleton(cache_key):\n",
    "    def inner_fn(fn):\n",
    "        @wraps(fn)\n",
    "        def wrapper(self, *args, **kwargs):\n",
    "            instance = getattr(self, cache_key)\n",
    "            if instance is not None:\n",
    "                return instance\n",
    "\n",
    "            instance = fn(self, *args, **kwargs)\n",
    "            setattr(self, cache_key, instance)\n",
    "            return instance\n",
    "        return wrapper\n",
    "    return inner_fn\n",
    "\n",
    "def get_module_device(module):\n",
    "    return next(module.parameters()).device\n",
    "\n",
    "def set_requires_grad(model, val):\n",
    "    for p in model.parameters():\n",
    "        p.requires_grad = val\n",
    "\n",
    "def MaybeSyncBatchnorm(is_distributed = None):\n",
    "    is_distributed = default(is_distributed, dist.is_initialized() and dist.get_world_size() > 1)\n",
    "    return nn.SyncBatchNorm if is_distributed else nn.BatchNorm1d\n",
    "\n",
    "# loss fn\n",
    "\n",
    "def loss_fn(x, y):\n",
    "    x = F.normalize(x, dim=-1, p=2)\n",
    "    y = F.normalize(y, dim=-1, p=2)\n",
    "    return 2 - 2 * (x * y).sum(dim=-1)\n",
    "\n",
    "# augmentation utils\n",
    "\n",
    "class RandomApply(nn.Module):\n",
    "    def __init__(self, fn, p):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "        self.p = p\n",
    "    def forward(self, x):\n",
    "        if random.random() > self.p:\n",
    "            return x\n",
    "        return self.fn(x)\n",
    "\n",
    "# exponential moving average\n",
    "\n",
    "class EMA():\n",
    "    def __init__(self, beta):\n",
    "        super().__init__()\n",
    "        self.beta = beta\n",
    "\n",
    "    def update_average(self, old, new):\n",
    "        if old is None:\n",
    "            return new\n",
    "        return old * self.beta + (1 - self.beta) * new\n",
    "    \n",
    "def update_moving_average(ema_updater, ma_model, current_model):\n",
    "    for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):\n",
    "        old_weight, up_weight = ma_params.data, current_params.data\n",
    "        ma_params.data = ema_updater.update_average(old_weight, up_weight)"
   ]
  },
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   },
   "source": [
    "# BYOL Models"
   ]
  },
  {
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   "source": [
    "# MLP class for projector and predictor\n",
    "\n",
    "def MLP(dim, projection_size, hidden_size=4096, sync_batchnorm=None):\n",
    "    return nn.Sequential(\n",
    "        nn.Linear(dim, hidden_size),\n",
    "        MaybeSyncBatchnorm(sync_batchnorm)(hidden_size),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.Linear(hidden_size, projection_size)\n",
    "    )\n",
    "\n",
    "def SimSiamMLP(dim, projection_size, hidden_size=4096, sync_batchnorm=None):\n",
    "    return nn.Sequential(\n",
    "        nn.Linear(dim, hidden_size, bias=False),\n",
    "        MaybeSyncBatchnorm(sync_batchnorm)(hidden_size),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.Linear(hidden_size, hidden_size, bias=False),\n",
    "        MaybeSyncBatchnorm(sync_batchnorm)(hidden_size),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.Linear(hidden_size, projection_size, bias=False),\n",
    "        MaybeSyncBatchnorm(sync_batchnorm)(projection_size, affine=False)\n",
    "    )\n",
    "\n",
    "# a wrapper class for the base neural network\n",
    "# will manage the interception of the hidden layer output\n",
    "# and pipe it into the projecter and predictor nets\n",
    "\n",
    "class NetWrapper(nn.Module):\n",
    "    def __init__(self, backbone, projection_size, projection_hidden_size, use_simsiam_mlp = False, sync_batchnorm = None):\n",
    "        super().__init__()\n",
    "        self.backbone = backbone\n",
    "        self.projector = None\n",
    "        self.projection_size = projection_size\n",
    "        self.projection_hidden_size = projection_hidden_size\n",
    "        self.use_simsiam_mlp = use_simsiam_mlp\n",
    "        self.sync_batchnorm = sync_batchnorm\n",
    "\n",
    "    @singleton('projector')\n",
    "    def _get_projector(self, hidden):\n",
    "        _, dim = hidden.shape\n",
    "        create_mlp_fn = MLP if not self.use_simsiam_mlp else SimSiamMLP\n",
    "        projector = create_mlp_fn(dim, self.projection_size, self.projection_hidden_size, sync_batchnorm = self.sync_batchnorm)\n",
    "        return projector.to(hidden)\n",
    "\n",
    "    def get_representation(self, x):\n",
    "        return self.backbone(x) #  (backbone + mlp) features: mlp can be trained instead of finetuning the backbone!\n",
    "\n",
    "    def forward(self, x, return_projection = True):\n",
    "        representation = self.get_representation(x)\n",
    "\n",
    "        if not return_projection:\n",
    "            return representation\n",
    "\n",
    "        projector = self._get_projector(representation)\n",
    "        projection = projector(representation)\n",
    "        return projection, representation\n",
    "\n",
    "\n",
    "# main class\n",
    "\n",
    "class BYOLProj(nn.Module):\n",
    "    \"\"\"BYOL model: only projection head can be trained, parameters of the backbone are frozen.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        net,\n",
    "        image_size,\n",
    "        projection_size = 224,\n",
    "        projection_hidden_size = 4096,\n",
    "        augment_fn = None,\n",
    "        augment_fn2 = None,\n",
    "        moving_average_decay = 0.99,\n",
    "        use_momentum = True,\n",
    "        sync_batchnorm = None\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.net = net\n",
    "\n",
    "        # --custom set of augmentations\n",
    "\n",
    "        DEFAULT_AUG = torch.nn.Sequential(\n",
    "            # RandomApply(\n",
    "            #     T.ColorJitter(0.8, 0.8, 0.8, 0.2),\n",
    "            #     p = 0.3\n",
    "            # ),\n",
    "            # T.RandomGrayscale(p=0.2),\n",
    "            T.RandomPerspective(distortion_scale=0.6, p=0.9),\n",
    "            T.RandomHorizontalFlip(),\n",
    "            # RandomApply(\n",
    "            #     T.GaussianBlur((3, 3), (1.0, 2.0)),\n",
    "            #     p = 0.2\n",
    "            # ),\n",
    "            T.RandomRotation(degrees=(0, 360)),\n",
    "            T.RandomResizedCrop((image_size, image_size)),\n",
    "            T.Normalize(\n",
    "                mean=torch.tensor([0.485, 0.456, 0.406]),\n",
    "                std=torch.tensor([0.229, 0.224, 0.225])),\n",
    "        )\n",
    "\n",
    "        self.augment1 = default(augment_fn, DEFAULT_AUG)\n",
    "        self.augment2 = default(augment_fn2, self.augment1)\n",
    "\n",
    "        self.online_encoder = NetWrapper(\n",
    "            net,\n",
    "            projection_size,\n",
    "            projection_hidden_size,\n",
    "            use_simsiam_mlp = not use_momentum,\n",
    "            sync_batchnorm = sync_batchnorm\n",
    "        )\n",
    "\n",
    "        self.use_momentum = use_momentum\n",
    "        self.target_encoder = None\n",
    "        self.target_ema_updater = EMA(moving_average_decay)\n",
    "\n",
    "        self.online_predictor = MLP(projection_size, projection_size, projection_hidden_size)\n",
    "\n",
    "        # get device of network and make wrapper same device\n",
    "        device = get_module_device(net)\n",
    "        self.to(device)\n",
    "\n",
    "        # send a mock image tensor to instantiate singleton parameters\n",
    "        self.forward(torch.randn(2, 3, image_size, image_size, device=device)) #! Trick to initialize weights!!!\n",
    "     \n",
    "\n",
    "    @singleton('target_encoder')\n",
    "    def _get_target_encoder(self):\n",
    "        target_encoder = copy.deepcopy(self.online_encoder)\n",
    "        set_requires_grad(target_encoder, False)\n",
    "        return target_encoder\n",
    "\n",
    "    def reset_moving_average(self):\n",
    "        del self.target_encoder\n",
    "        self.target_encoder = None\n",
    "\n",
    "    def update_moving_average(self):\n",
    "        assert self.use_momentum, 'you do not need to update the moving average, since you have turned off momentum for the target encoder'\n",
    "        assert self.target_encoder is not None, 'target encoder has not been created yet'\n",
    "        update_moving_average(self.target_ema_updater, self.target_encoder, self.online_encoder)\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        x,\n",
    "        return_embedding = False,\n",
    "        return_projection = True\n",
    "    ):\n",
    "        assert not (self.training and x.shape[0] == 1), 'you must have greater than 1 sample when training, due to the batchnorm in the projection layer'\n",
    "\n",
    "        if return_embedding:\n",
    "            return self.online_encoder(x, return_projection = return_projection)\n",
    "        \n",
    "        # -- create two views: anchor view, target view\n",
    "        image_one, image_two = self.augment1(x), self.augment2(x)\n",
    "\n",
    "        images = torch.cat((image_one, image_two), dim = 0)\n",
    "\n",
    "        online_projections, _ = self.online_encoder(images)\n",
    "        online_predictions = self.online_predictor(online_projections)\n",
    "\n",
    "        online_pred_one, online_pred_two = online_predictions.chunk(2, dim = 0)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            target_encoder = self._get_target_encoder() if self.use_momentum else self.online_encoder\n",
    "\n",
    "            target_projections, _ = target_encoder(images)\n",
    "            target_projections = target_projections.detach()\n",
    "\n",
    "            target_proj_one, target_proj_two = target_projections.chunk(2, dim = 0)\n",
    "\n",
    "        loss_one = loss_fn(online_pred_one, target_proj_two.detach())\n",
    "        loss_two = loss_fn(online_pred_two, target_proj_one.detach())\n",
    "\n",
    "        loss = loss_one + loss_two\n",
    "        return loss.mean()\n"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Models"
   ]
  },
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   },
   "source": [
    "## WideResNet50\n"
   ]
  },
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   "source": [
    "########################### - Imports - #####################################\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "from torchvision import models\n",
    "\n",
    "#############################################################################\n",
    "\n",
    "\n",
    "########################### - Models - #####################################\n",
    "class WideResnet50(nn.Module):\n",
    "    def __init__(self, embedding_size, is_norm=True, bn_freeze=True):\n",
    "        super(WideResnet50, self).__init__()\n",
    "\n",
    "        self.embedding_size = embedding_size\n",
    "        self.is_norm = is_norm\n",
    "        self.model = models.wide_resnet50_2(pretrained=True)\n",
    "\n",
    "        # Freezing the model weights in the backbone\n",
    "        for param in self.model.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "        # Linear projection\n",
    "        # self.model.fc = nn.Linear(2048, self.embedding_size)\n",
    "        self.model.fc = nn.Sequential(\n",
    "                            nn.Linear(2048, self.embedding_size),\n",
    "                            # nn.BatchNorm1d(self.embedding_size),\n",
    "                            # nn.ReLU(inplace=True),\n",
    "                            # nn.Linear(self.embedding_size, self.embedding_size)\n",
    "                        )\n",
    "        self._initialize_weights()\n",
    "\n",
    "        \n",
    "        # Freezing the layernomralization parameters\n",
    "        if bn_freeze:\n",
    "            for m in self.model.modules():\n",
    "                if isinstance(m, nn.BatchNorm2d):\n",
    "                    m.eval()\n",
    "                    m.weight.requires_grad_(False)\n",
    "                    m.bias.requires_grad_(False)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "\n",
    "        if self.is_norm:\n",
    "            x = self.l2_norm(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        # init.kaiming_normal_(self.model.fc.weight, mode='fan_out')\n",
    "        # init.constant_(self.model.fc.bias, 0)\n",
    "        for m in self.model.fc.modules():\n",
    "            if isinstance(m, torch.nn.Linear):\n",
    "                torch.nn.init.kaiming_normal_(m.weight)\n",
    "                m.weight.requires_grad = True\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "                    m.bias.requires_grad = True\n",
    "\n",
    "\n",
    "    def l2_norm(self,input):\n",
    "        input_size = input.size()\n",
    "        buffer = torch.pow(input, 2)\n",
    "\n",
    "        normp = torch.sum(buffer, 1).add_(1e-12)\n",
    "        norm = torch.sqrt(normp)\n",
    "\n",
    "        _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n",
    "\n",
    "        output = _output.view(input_size)\n",
    "\n",
    "        return output\n",
    "    \n",
    "#############################################################################\n",
    "\n",
    "\n",
    "########################### - Testing - #####################################\n",
    "# x = torch.randn(3, 3, 224, 224)\n",
    "# model = WideResnet50(400, True, True)\n",
    "# print('\\n==============\\n')\n",
    "# print(model)\n",
    "# print(model(x).shape)"
   ]
  },
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    "tags": []
   },
   "source": [
    "## DINOv2"
   ]
  },
  {
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   "id": "7b32149b",
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   "source": [
    "########################### - Imports - #####################################\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "#############################################################################\n",
    "\n",
    "\n",
    "########################### - Helper Functions - #####################################\n",
    "def load_model(model_type='dinov2_vitg14'):\n",
    "    \"\"\"Loading the specified pretrained model from DINOv2 repository\n",
    "        Args:\n",
    "            model_type: name of the model to be loaded, default is 'dinov2_vitg14'\n",
    "\n",
    "        Ref:\n",
    "            https://github.com/facebookresearch/dinov2/blob/main/MODEL_CARD.md\n",
    "\n",
    "        Returns:\n",
    "            model: DINOv2 model\n",
    "            embed_size: size of the embedding at the last layer\n",
    "    \"\"\"\n",
    "\n",
    "    # Dowloading the specified dinov2 model\n",
    "    model = torch.hub.load('facebookresearch/dinov2', model_type)\n",
    "    # print('\\nDINOv2 model summary:\\n', model)\n",
    "\n",
    "    # Assign the last_layer model type based on the model type\n",
    "    if model_type == 'dinov2_vitg14':\n",
    "        embed_size = 1536\n",
    "\n",
    "    elif model_type == 'dinov2_vitl14':\n",
    "        embed_size = 1024\n",
    "\n",
    "    elif model_type == 'dinov2_vitb14':\n",
    "        embed_size = 768\n",
    "    \n",
    "    elif model_type == 'dinov2_vits14':\n",
    "        embed_size = 384\n",
    "\n",
    "    return model, embed_size\n",
    "#############################################################################\n",
    "\n",
    "\n",
    "########################### - Models - #####################################\n",
    "class DINOv2(nn.Module):\n",
    "    def __init__(self, model_type, embedding_size,  is_norm=True, ln_freeze=True):\n",
    "        super(DINOv2, self).__init__()\n",
    "\n",
    "        self.embedding_size = embedding_size # For embedding layer\n",
    "        self.is_norm = is_norm\n",
    "\n",
    "        # Load the pretrained backbone model\n",
    "        backbone, last_layer_embed_size = load_model(model_type)\n",
    "        self.model = backbone\n",
    "\n",
    "        # Freezing the model weights in the backbone\n",
    "        for param in self.model.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "        # Linear projection\n",
    "        # self.model.head = nn.Linear(last_layer_embed_size, self.embedding_size)\n",
    "        self.model.head = nn.Sequential(\n",
    "                            nn.Linear(last_layer_embed_size, self.embedding_size),\n",
    "                            # nn.BatchNorm1d(self.embedding_size),\n",
    "                            # nn.ReLU(inplace=True),\n",
    "                            # nn.Linear(self.embedding_size, self.embedding_size)\n",
    "                        )\n",
    "        self._initialize_weights()\n",
    "\n",
    "        # Freezing the layernomralization parameters\n",
    "        if ln_freeze:\n",
    "            for m in self.model.modules():\n",
    "                if isinstance(m, nn.LayerNorm):\n",
    "                    m.eval()\n",
    "                    m.weight.requires_grad_(False)\n",
    "                    m.bias.requires_grad_(False)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.model(x)\n",
    "\n",
    "        if self.is_norm:\n",
    "            x = self.l2_norm(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        # init.kaiming_normal_(self.model.head.weight, mode='fan_out')\n",
    "        # init.constant_(self.model.head.bias, 0)\n",
    "        for m in self.model.head.modules():\n",
    "            if isinstance(m, torch.nn.Linear):\n",
    "                torch.nn.init.kaiming_normal_(m.weight)\n",
    "                m.weight.requires_grad = True\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "                    m.bias.requires_grad = True\n",
    "\n",
    "\n",
    "    def l2_norm(self,input):\n",
    "        input_size = input.size()\n",
    "        buffer = torch.pow(input, 2)\n",
    "\n",
    "        normp = torch.sum(buffer, 1).add_(1e-12)\n",
    "        norm = torch.sqrt(normp)\n",
    "\n",
    "        _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n",
    "\n",
    "        output = _output.view(input_size)\n",
    "\n",
    "        return output\n",
    "\n",
    "#############################################################################\n",
    "\n",
    "\n",
    "########################### - Testing - #####################################\n",
    "# x = torch.randn(1, 3, 224, 224)\n",
    "# model = DINOv2('dinov2_vitb14', 400, True, True)\n",
    "# print('\\n==============\\n')\n",
    "# print(model)\n",
    "# print(model(x).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9adfcbcb",
   "metadata": {
    "papermill": {
     "duration": 0.006699,
     "end_time": "2024-03-31T11:00:57.011862",
     "exception": false,
     "start_time": "2024-03-31T11:00:57.005163",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Traning Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c46943d0",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lightning\r\n",
      "  Downloading lightning-2.2.1-py3-none-any.whl.metadata (56 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.8/56.8 kB\u001b[0m \u001b[31m783.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: PyYAML<8.0,>=5.4 in /opt/conda/lib/python3.10/site-packages (from lightning) (6.0.1)\r\n",
      "Requirement already satisfied: fsspec<2025.0,>=2022.5.0 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]<2025.0,>=2022.5.0->lightning) (2024.3.0)\r\n",
      "Requirement already satisfied: lightning-utilities<2.0,>=0.8.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (0.10.1)\r\n",
      "Requirement already satisfied: numpy<3.0,>=1.17.2 in /opt/conda/lib/python3.10/site-packages (from lightning) (1.26.4)\r\n",
      "Requirement already satisfied: packaging<25.0,>=20.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (21.3)\r\n",
      "Requirement already satisfied: torch<4.0,>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (2.1.2)\r\n",
      "Requirement already satisfied: torchmetrics<3.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (1.3.2)\r\n",
      "Requirement already satisfied: tqdm<6.0,>=4.57.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (4.66.1)\r\n",
      "Requirement already satisfied: typing-extensions<6.0,>=4.4.0 in /opt/conda/lib/python3.10/site-packages (from lightning) (4.9.0)\r\n",
      "Requirement already satisfied: pytorch-lightning in /opt/conda/lib/python3.10/site-packages (from lightning) (2.2.1)\r\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]<2025.0,>=2022.5.0->lightning) (3.9.1)\r\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from lightning-utilities<2.0,>=0.8.0->lightning) (69.0.3)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging<25.0,>=20.0->lightning) (3.1.1)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch<4.0,>=1.13.0->lightning) (3.13.1)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch<4.0,>=1.13.0->lightning) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch<4.0,>=1.13.0->lightning) (3.2.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch<4.0,>=1.13.0->lightning) (3.1.2)\r\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (23.2.0)\r\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (6.0.4)\r\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (1.9.3)\r\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (1.4.1)\r\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (1.3.1)\r\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (4.0.3)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch<4.0,>=1.13.0->lightning) (2.1.3)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch<4.0,>=1.13.0->lightning) (1.3.0)\r\n",
      "Requirement already satisfied: idna>=2.0 in /opt/conda/lib/python3.10/site-packages (from yarl<2.0,>=1.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>=2022.5.0->lightning) (3.6)\r\n",
      "Downloading lightning-2.2.1-py3-none-any.whl (2.1 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: lightning\r\n",
      "Successfully installed lightning-2.2.1\r\n"
     ]
    }
   ],
   "source": [
    "!pip install lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d2a5b27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T11:01:16.803553Z",
     "iopub.status.busy": "2024-03-31T11:01:16.803234Z",
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     "shell.execute_reply": "2024-03-31T11:01:21.197265Z"
    },
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     "duration": 4.407765,
     "end_time": "2024-03-31T11:01:21.201017",
     "exception": false,
     "start_time": "2024-03-31T11:01:16.793252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import numpy as np\n",
    "import multiprocessing\n",
    "from pathlib import Path\n",
    "from PIL import Image, ImageFilter\n",
    "\n",
    "import logging\n",
    "\n",
    "import torch\n",
    "from torchvision import models, transforms\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "import lightning as l\n",
    "from lightning.pytorch.callbacks.early_stopping import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3d7352c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T11:01:21.221124Z",
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   },
   "outputs": [],
   "source": [
    "# seeding\n",
    "_GLOBAL_SEED = 0\n",
    "np.random.seed(_GLOBAL_SEED)\n",
    "torch.manual_seed(_GLOBAL_SEED)\n",
    "torch.backends.cudnn.benchmark = True\n",
    "\n",
    "\n",
    "# logging\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logger = logging.getLogger()\n",
    "\n",
    "# constants\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS     = 5 #50\n",
    "LR         = 1e-4\n",
    "lr_decay_step = 10\n",
    "lr_decay_gamma = 0.5\n",
    "NUM_GPUS   = 2\n",
    "IMAGE_SIZE = 224 # 256\n",
    "IMAGE_CROP_SIZE = 224\n",
    "IMAGE_RESIZE = 256\n",
    "IMAGE_EXTS = ['.jpg', '.png', '.jpeg']\n",
    "NUM_WORKERS = multiprocessing.cpu_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7f9f573",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# pytorch lightning module\n",
    "\n",
    "class SelfSupervisedLearner(l.LightningModule):\n",
    "    def __init__(self, net, **kwargs):\n",
    "        super().__init__()\n",
    "        # self.save_hyperparameters()\n",
    "        self.learner = BYOLProj(net, **kwargs)\n",
    "\n",
    "    def forward(self, images):\n",
    "        return self.learner(images)\n",
    "\n",
    "    def training_step(self, images, _):\n",
    "        loss_list = []\n",
    "        for image_batch in images:\n",
    "            multi_view_loss = self.forward(image_batch)\n",
    "            loss_list.append(multi_view_loss)\n",
    "\n",
    "        loss = sum(loss_list) / len(loss_list)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True, on_epoch=True, on_step=True, sync_dist=True)\n",
    "        return {'loss': loss}\n",
    "\n",
    "    def validation_step(self, images, _):\n",
    "        val_loss = self.forward(images)\n",
    "        self.log(\"val_loss\", val_loss, prog_bar=True, on_epoch=True, on_step=True, sync_dist=True)\n",
    "        self.log('metric_to_track', val_loss, sync_dist=True)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=LR, weight_decay=1.5e-6)\n",
    "        # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step, gamma = lr_decay_gamma)\n",
    "        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=lr_decay_step, eta_min=0)\n",
    "        lr_scheduler_config = {\n",
    "            # REQUIRED: The scheduler instance\n",
    "            \"scheduler\": scheduler,\n",
    "            # The unit of the scheduler's step size, could also be 'step'.\n",
    "            # 'epoch' updates the scheduler on epoch end whereas 'step'\n",
    "            # updates it after a optimizer update.\n",
    "            \"interval\": \"epoch\",\n",
    "            # How many epochs/steps should pass between calls to\n",
    "            # `scheduler.step()`. 1 corresponds to updating the learning\n",
    "            # rate after every epoch/step.\n",
    "            \"frequency\": 1,\n",
    "            # Metric to to monitor for schedulers like `ReduceLROnPlateau`\n",
    "            \"monitor\": \"metric_to_track\",\n",
    "            # If set to `True`, will enforce that the value specified 'monitor'\n",
    "            # is available when the scheduler is updated, thus stopping\n",
    "            # training if not found. If set to `False`, it will only produce a warning\n",
    "            \"strict\": True,\n",
    "            # If using the `LearningRateMonitor` callback to monitor the\n",
    "            # learning rate progress, this keyword can be used to specify\n",
    "            # a custom logged name\n",
    "            \"name\": None,\n",
    "        }\n",
    "        return {\n",
    "            \"optimizer\": optimizer,\n",
    "            \"lr_scheduler\": lr_scheduler_config\n",
    "        }\n",
    "\n",
    "    def on_before_zero_grad(self, _):\n",
    "        if self.learner.use_momentum:\n",
    "            self.learner.update_moving_average()\n",
    "\n",
    "\n",
    "# -- dataset transformations\n",
    "def make_transforms(\n",
    "    rand_size=224,\n",
    "    focal_size=224,\n",
    "    rand_crop_scale=(0.3, 1.0),\n",
    "    focal_crop_scale=(0.05, 0.3),\n",
    "    color_jitter=1.0,\n",
    "    rand_views=2,\n",
    "    focal_views=10,\n",
    "):\n",
    "\n",
    "    def get_color_distortion(s=1.0):\n",
    "        # s is the strength of color distortion.\n",
    "        color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s, 0.2*s)\n",
    "        rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)\n",
    "        rnd_gray = transforms.RandomGrayscale(p=0.2)\n",
    "        color_distort = transforms.Compose([\n",
    "            rnd_color_jitter,\n",
    "            rnd_gray])\n",
    "        return color_distort\n",
    "\n",
    "    rand_transform = transforms.Compose([\n",
    "        transforms.RandomResizedCrop(rand_size, scale=rand_crop_scale),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        # get_color_distortion(s=color_jitter),\n",
    "        GaussianBlur(p=0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(\n",
    "            (0.485, 0.456, 0.406),\n",
    "            (0.229, 0.224, 0.225))\n",
    "    ])\n",
    "    focal_transform = transforms.Compose([\n",
    "        transforms.RandomResizedCrop(focal_size, scale=focal_crop_scale),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        # get_color_distortion(s=color_jitter),\n",
    "        GaussianBlur(p=0.5),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(\n",
    "            (0.485, 0.456, 0.406),\n",
    "            (0.229, 0.224, 0.225))\n",
    "    ])\n",
    "\n",
    "    transform = MultiViewTransform(\n",
    "        rand_transform=rand_transform,\n",
    "        focal_transform=focal_transform,\n",
    "        rand_views=rand_views,\n",
    "        focal_views=focal_views\n",
    "    )\n",
    "    return transform\n",
    "\n",
    "\n",
    "class MultiViewTransform(object):\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        rand_transform=None,\n",
    "        focal_transform=None,\n",
    "        rand_views=1,\n",
    "        focal_views=1,\n",
    "    ):\n",
    "        self.rand_views = rand_views\n",
    "        self.focal_views = focal_views\n",
    "        self.rand_transform = rand_transform\n",
    "        self.focal_transform = focal_transform\n",
    "\n",
    "    def __call__(self, img):\n",
    "        img_views = []\n",
    "\n",
    "        # -- generate random views\n",
    "        if self.rand_views > 0:\n",
    "            img_views += [self.rand_transform(img) for i in range(self.rand_views)]\n",
    "\n",
    "        # -- generate focal views\n",
    "        if self.focal_views > 0:\n",
    "            img_views += [self.focal_transform(img) for i in range(self.focal_views)]\n",
    "\n",
    "        return img_views\n",
    "\n",
    "\n",
    "class GaussianBlur(object):\n",
    "    def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):\n",
    "        self.prob = p\n",
    "        self.radius_min = radius_min\n",
    "        self.radius_max = radius_max\n",
    "\n",
    "    def __call__(self, img):\n",
    "        if torch.bernoulli(torch.tensor(self.prob)) == 0:\n",
    "            return img\n",
    "\n",
    "        radius = self.radius_min + torch.rand(1) * (self.radius_max - self.radius_min)\n",
    "        return img.filter(ImageFilter.GaussianBlur(radius=radius))\n",
    "\n",
    "\n",
    "# images dataset\n",
    "class Identity(): # used for skipping transforms\n",
    "    def __call__(self, im):\n",
    "        return im\n",
    "    \n",
    "class ScaleIntensities():\n",
    "    def __init__(self, in_range, out_range):\n",
    "        \"\"\" Scales intensities. For example [-1, 1] -> [0, 255].\"\"\"\n",
    "        self.in_range = in_range\n",
    "        self.out_range = out_range\n",
    "\n",
    "    def __oldcall__(self, tensor):\n",
    "        tensor.mul_(255)\n",
    "        return tensor\n",
    "\n",
    "    def __call__(self, tensor):\n",
    "        tensor = (\n",
    "            tensor - self.in_range[0]\n",
    "        ) / (\n",
    "            self.in_range[1] - self.in_range[0]\n",
    "        ) * (\n",
    "            self.out_range[1] - self.out_range[0]\n",
    "        ) + self.out_range[0]\n",
    "        return tensor\n",
    "\n",
    "def expand_greyscale(t):\n",
    "    return t.expand(3, -1, -1)\n",
    "\n",
    "class ImagesDataset(Dataset):\n",
    "    def __init__(self, folder, image_size, transform=None, is_train=True):\n",
    "        super().__init__()\n",
    "        self.folder = folder\n",
    "        self.paths = []\n",
    "\n",
    "        for path in Path(f'{folder}').glob('**/*'):\n",
    "            _, ext = os.path.splitext(path)\n",
    "            if ext.lower() in IMAGE_EXTS:\n",
    "                self.paths.append(path)\n",
    "\n",
    "        print(f'{len(self.paths)} images found')\n",
    "\n",
    "        # self.transform = transforms.Compose([\n",
    "        #     transforms.RandomResizedCrop(IMAGE_CROP_SIZE) if is_train else Identity(), \n",
    "        #     transforms.RandomRotation(degrees=(0, 360)),\n",
    "        #     transforms.Resize(IMAGE_RESIZE) if not is_train else Identity(),\n",
    "        #     transforms.CenterCrop(image_size) if not is_train else Identity(),\n",
    "        #     transforms.ToTensor(),\n",
    "        #     # ScaleIntensities([0, 1], [0, 255]),\n",
    "        #     transforms.Lambda(expand_greyscale) if is_train else Identity()\n",
    "        # ])\n",
    "        if transform is None:\n",
    "            self.transform = transforms.Compose([\n",
    "                transforms.Resize(image_size),\n",
    "                transforms.CenterCrop(image_size),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Lambda(expand_greyscale)\n",
    "            ])\n",
    "        else:\n",
    "            self.transform = transform # focal + random data augmentations\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.paths)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        path = self.paths[index]\n",
    "        img = Image.open(path)\n",
    "        img = img.convert('RGB')\n",
    "        return self.transform(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13cca148",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T11:01:21.314938Z",
     "iopub.status.busy": "2024-03-31T11:01:21.314152Z",
     "iopub.status.idle": "2024-03-31T11:01:21.319438Z",
     "shell.execute_reply": "2024-03-31T11:01:21.318484Z"
    },
    "papermill": {
     "duration": 0.018603,
     "end_time": "2024-03-31T11:01:21.321662",
     "exception": false,
     "start_time": "2024-03-31T11:01:21.303059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_model(model_name):\n",
    "    if model_name == 'dinov2':\n",
    "        return DINOv2(model_type='dinov2_vitb14', embedding_size=256)\n",
    "#     elif model_name == 'resnet50':\n",
    "#         return Resnet50(embedding_size=256)\n",
    "    elif model_name == 'wideresnet50':\n",
    "        return WideResnet50(embedding_size=256)\n",
    "    else:\n",
    "        raise ValueError('Unknown model type!!!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f5a2f7",
   "metadata": {
    "papermill": {
     "duration": 0.008525,
     "end_time": "2024-03-31T11:01:21.339028",
     "exception": false,
     "start_time": "2024-03-31T11:01:21.330503",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "165a001d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T11:01:21.357639Z",
     "iopub.status.busy": "2024-03-31T11:01:21.356965Z",
     "iopub.status.idle": "2024-03-31T11:01:21.366124Z",
     "shell.execute_reply": "2024-03-31T11:01:21.365251Z"
    },
    "papermill": {
     "duration": 0.021187,
     "end_time": "2024-03-31T11:01:21.368747",
     "exception": false,
     "start_time": "2024-03-31T11:01:21.347560",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Args(prog='ipykernel_launcher.py', usage=None, description=None, formatter_class=<class 'argparse.HelpFormatter'>, conflict_handler='error', add_help=True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Args(argparse.ArgumentParser):\n",
    "    def __init__(self, model_type, image_folder, use_momentum):\n",
    "        super().__init__()\n",
    "        self.model_type = model_type\n",
    "        self.image_folder = image_folder\n",
    "        self.use_momentum = use_momentum\n",
    "        \n",
    "\n",
    "folder_path = '/kaggle/input/product-image-amazone/Categories'\n",
    "args = Args(model_type = 'wideresnet50',\n",
    "           image_folder = folder_path,\n",
    "           use_momentum = False)\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a283d32e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T11:01:21.387820Z",
     "iopub.status.busy": "2024-03-31T11:01:21.387546Z",
     "iopub.status.idle": "2024-03-31T12:26:29.860205Z",
     "shell.execute_reply": "2024-03-31T12:26:29.859047Z"
    },
    "papermill": {
     "duration": 5108.485658,
     "end_time": "2024-03-31T12:26:29.862959",
     "exception": false,
     "start_time": "2024-03-31T11:01:21.377301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7020 images found\n",
      "1755 images found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1`. You can also use `weights=Wide_ResNet50_2_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth\" to /root/.cache/torch/hub/checkpoints/wide_resnet50_2-95faca4d.pth\n",
      "100%|██████████| 132M/132M [00:01<00:00, 100MB/s]\n",
      "/opt/conda/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SelfSupervisedLearner(\n",
      "  (learner): BYOLProj(\n",
      "    (net): WideResnet50(\n",
      "      (model): ResNet(\n",
      "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "        (layer1): Sequential(\n",
      "          (0): Bottleneck(\n",
      "            (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "            (downsample): Sequential(\n",
      "              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): Bottleneck(\n",
      "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (2): Bottleneck(\n",
      "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "        )\n",
      "        (layer2): Sequential(\n",
      "          (0): Bottleneck(\n",
      "            (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "            (downsample): Sequential(\n",
      "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): Bottleneck(\n",
      "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (2): Bottleneck(\n",
      "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (3): Bottleneck(\n",
      "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "        )\n",
      "        (layer3): Sequential(\n",
      "          (0): Bottleneck(\n",
      "            (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "            (downsample): Sequential(\n",
      "              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (2): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (3): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (4): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (5): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "        )\n",
      "        (layer4): Sequential(\n",
      "          (0): Bottleneck(\n",
      "            (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "            (downsample): Sequential(\n",
      "              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "              (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): Bottleneck(\n",
      "            (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "          (2): Bottleneck(\n",
      "            (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            (relu): ReLU(inplace=True)\n",
      "          )\n",
      "        )\n",
      "        (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "        (fc): Sequential(\n",
      "          (0): Linear(in_features=2048, out_features=256, bias=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (augment1): Sequential(\n",
      "      (0): RandomPerspective(p=0.9)\n",
      "      (1): RandomHorizontalFlip(p=0.5)\n",
      "      (2): RandomRotation(degrees=[0.0, 360.0], interpolation=nearest, expand=False, fill=0)\n",
      "      (3): RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=warn)\n",
      "      (4): Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))\n",
      "    )\n",
      "    (augment2): Sequential(\n",
      "      (0): RandomPerspective(p=0.9)\n",
      "      (1): RandomHorizontalFlip(p=0.5)\n",
      "      (2): RandomRotation(degrees=[0.0, 360.0], interpolation=nearest, expand=False, fill=0)\n",
      "      (3): RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=warn)\n",
      "      (4): Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))\n",
      "    )\n",
      "    (online_encoder): NetWrapper(\n",
      "      (backbone): WideResnet50(\n",
      "        (model): ResNet(\n",
      "          (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace=True)\n",
      "          (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "          (layer1): Sequential(\n",
      "            (0): Bottleneck(\n",
      "              (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "              (downsample): Sequential(\n",
      "                (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "                (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              )\n",
      "            )\n",
      "            (1): Bottleneck(\n",
      "              (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (2): Bottleneck(\n",
      "              (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "          )\n",
      "          (layer2): Sequential(\n",
      "            (0): Bottleneck(\n",
      "              (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "              (downsample): Sequential(\n",
      "                (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "                (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              )\n",
      "            )\n",
      "            (1): Bottleneck(\n",
      "              (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (2): Bottleneck(\n",
      "              (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (3): Bottleneck(\n",
      "              (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "          )\n",
      "          (layer3): Sequential(\n",
      "            (0): Bottleneck(\n",
      "              (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "              (downsample): Sequential(\n",
      "                (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "                (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              )\n",
      "            )\n",
      "            (1): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (2): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (3): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (4): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (5): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "          )\n",
      "          (layer4): Sequential(\n",
      "            (0): Bottleneck(\n",
      "              (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "              (downsample): Sequential(\n",
      "                (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "                (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              )\n",
      "            )\n",
      "            (1): Bottleneck(\n",
      "              (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "            (2): Bottleneck(\n",
      "              (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (relu): ReLU(inplace=True)\n",
      "            )\n",
      "          )\n",
      "          (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "          (fc): Sequential(\n",
      "            (0): Linear(in_features=2048, out_features=256, bias=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (projector): Sequential(\n",
      "        (0): Linear(in_features=256, out_features=4096, bias=False)\n",
      "        (1): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU(inplace=True)\n",
      "        (3): Linear(in_features=4096, out_features=4096, bias=False)\n",
      "        (4): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (5): ReLU(inplace=True)\n",
      "        (6): Linear(in_features=4096, out_features=256, bias=False)\n",
      "        (7): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (online_predictor): Sequential(\n",
      "      (0): Linear(in_features=256, out_features=4096, bias=True)\n",
      "      (1): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU(inplace=True)\n",
      "      (3): Linear(in_features=4096, out_features=256, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "Args(prog='ipykernel_launcher.py', usage=None, description=None, formatter_class=<class 'argparse.HelpFormatter'>, conflict_handler='error', add_help=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: GPU available: True (cuda), used: True\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "INFO: Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2\n",
      "INFO: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2\n",
      "WARNING: Missing logger folder: /kaggle/working/lightning_logs\n",
      "INFO: ----------------------------------------------------------------------------------------------------\n",
      "distributed_backend=nccl\n",
      "All distributed processes registered. Starting with 2 processes\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "WARNING: Missing logger folder: /kaggle/working/lightning_logs\n",
      "2024-03-31 11:01:37.927372: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-31 11:01:37.927554: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-31 11:01:38.193339: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "INFO: LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "INFO: \n",
      "  | Name    | Type     | Params\n",
      "-------------------------------------\n",
      "0 | learner | BYOLProj | 88.4 M\n",
      "-------------------------------------\n",
      "21.5 M    Trainable params\n",
      "66.8 M    Non-trainable params\n",
      "88.4 M    Total params\n",
      "353.437   Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63302c52c96445589871f6cbca1f814c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29d61f37ccac49dcaa8386628e5cb654",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    {
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     "text": [
      "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n"
     ]
    }
   ],
   "source": [
    "transform = make_transforms()\n",
    "\n",
    "# Dataset: [train, val] -> train [0, 1, 2] and val [4, 5, 6]\n",
    "train_dataset = ImagesDataset(os.path.join(args.image_folder, 'train_set'), IMAGE_SIZE, transform=transform)\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, shuffle=True)\n",
    "val_dataset = ImagesDataset(os.path.join(args.image_folder, 'valid_set'), IMAGE_SIZE, is_train=False)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, shuffle=False)\n",
    "\n",
    "base_model = get_model(args.model_type)\n",
    "\n",
    "\n",
    "model = SelfSupervisedLearner(\n",
    "    base_model,\n",
    "    image_size = IMAGE_SIZE,\n",
    "    projection_size = 256,\n",
    "    projection_hidden_size = 4096,\n",
    "    moving_average_decay = 0.99,\n",
    "    use_momentum = args.use_momentum\n",
    ")\n",
    "logger.info('#### - Model - ###')\n",
    "print(model)\n",
    "logger.info('#### - Arguements - ###')\n",
    "print(args)\n",
    "\n",
    "# -- callbacks\n",
    "early_stop_fn = EarlyStopping(monitor='val_loss', mode='min', patience=5)\n",
    "trainer = l.Trainer(\n",
    "                    callbacks=[early_stop_fn],\n",
    "                     max_epochs=EPOCHS,\n",
    "                     accelerator=\"auto\",\n",
    "                     accumulate_grad_batches = 1,\n",
    "                     sync_batchnorm = True,\n",
    "                     log_every_n_steps=1,\n",
    "                     devices=-1)\n",
    "\n",
    "trainer.fit(model, train_loader, val_loader)"
   ]
  },
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    {
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     "text": [
      "  adding: kaggle/working/ (stored 0%)\r\n",
      "  adding: kaggle/working/__notebook__.ipynb (deflated 85%)\r\n",
      "  adding: kaggle/working/lightning_logs/ (stored 0%)\r\n",
      "  adding: kaggle/working/lightning_logs/version_0/ (stored 0%)\r\n",
      "  adding: kaggle/working/lightning_logs/version_0/checkpoints/ (stored 0%)\r\n",
      "  adding: kaggle/working/lightning_logs/version_0/checkpoints/epoch=4-step=550.ckpt (deflated 26%)\r\n",
      "  adding: kaggle/working/lightning_logs/version_0/hparams.yaml (stored 0%)\r\n",
      "  adding: kaggle/working/lightning_logs/version_0/events.out.tfevents.1711882911.9bd9fa44b6e7.57.0 (deflated 69%)\r\n"
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     "text": [
      "__notebook__.ipynb  file.zip  lightning_logs\r\n"
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   "source": [
    "!ls /kaggle/working"
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  {
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   "id": "2de870f3",
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     "status": "completed"
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    {
     "data": {
      "text/html": [
       "<a href='file.zip' target='_blank'>file.zip</a><br>"
      ],
      "text/plain": [
       "/kaggle/working/file.zip"
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     },
     "execution_count": 15,
     "metadata": {},
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   "source": [
    "from IPython.display import FileLink\n",
    "FileLink(r'file.zip')"
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