MNIST_Segmentation_Dataset_SMPModel.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1iiHId1CY7dI55axABdRtolqvpFqz2TWo","timestamp":1714216594507}],"machine_shape":"hm","authorship_tag":"ABX9TyM9SKQMZvOQqZHsSBki54mo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["# !pip install segmentation-models-pytorch\n","# !pip install lightning"],"metadata":{"id":"xBdnvJn-P1X6","executionInfo":{"status":"ok","timestamp":1714221966744,"user_tz":-60,"elapsed":3,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"metadata":{"id":"NyH_uRr3MQaW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222000243,"user_tz":-60,"elapsed":27180,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"362fa66f-24f1-49e1-cf2f-db771c915b01"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["# %cd /content/gdrive/MyDrive/Colab Notebooks\n","\n","# %mkdir TorchModel\n","\n","%cd /content/gdrive/MyDrive/Colab Notebooks/TorchModel/"],"metadata":{"id":"A8JZxC41HDOx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222004796,"user_tz":-60,"elapsed":451,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"c130833e-c552-4835-ae3f-534d8c90b4f9"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/gdrive/MyDrive/Colab Notebooks/TorchModel\n"]}]},{"cell_type":"code","source":["import os\n","import numpy as np\n","from numpy import random\n","import matplotlib.pyplot as plt\n","\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\n","from torchvision.transforms import v2\n","from torchvision import transforms, datasets\n","from torchsummary import summary\n","\n","import lightning.pytorch as pl\n","from lightning.pytorch.loggers import TensorBoardLogger, CSVLogger\n","from lightning.pytorch.callbacks import ModelCheckpoint\n","from lightning.pytorch import Trainer, LightningModule\n","\n","import segmentation_models_pytorch as smp\n","\n","RANDOM_SEED = 123\n","\n","pl.seed_everything(RANDOM_SEED)\n","\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"],"metadata":{"id":"BY2UI_x7HFzB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222019219,"user_tz":-60,"elapsed":11129,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"16f8e0da-820f-40c7-bd27-4e3e99989393"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n"]}]},{"cell_type":"code","source":["class SegmentationMNIST(Dataset):\n","  def __init__(self, root:str, train:bool, image_size=(128, 128), transform=None, target_transform = None, ndigits = (5, 8), max_iou=0.1):\n","    \"\"\"\n","    Args:\n","    - root: the route for the MNIST dataset. It will be downloaded if it does not exist\n","    - train: True for training set, False for test set\n","    - image_size: tuple with the dataset image size\n","    - transform: the transforms to be applied to the input image\n","    - target_transform: the transforms to be applied to the label image\n","    - ndigits: tuple with the mininum and maximum number of digits per image\n","    - max_iou: maximum IOU between digit bounding boxes\n","    \"\"\"\n","\n","    self.transform = transform\n","    self.target_transform = target_transform\n","    self.image_template = torch.zeros((1, *image_size), dtype=torch.float32)\n","    self.target_tempalte = torch.zeros((1, * image_size), dtype=torch.uint8)\n","    self.max_iou = 0.2\n","\n","    mnist_transform = v2.Compose([\n","        v2.Pad(4),\n","        v2.RandomAffine(degrees=15, translate=(0.2, 0.2), scale=(0.5, 1.5), shear=5),\n","        v2.Resize((48, 48)),\n","        v2.ToImage(),\n","        v2.ToDtype(torch.float32, scale=True),\n","    ])\n","\n","    self.mnist = datasets.MNIST(root, train, download=True, transform=mnist_transform)\n","    self.index = random.permutation(len(self.mnist))\n","    self.length = len(self.mnist)\n","\n","    # Compute the number of digits in each image, and the total number of images\n","    self.num_digits = []\n","    remaining = len(self.mnist)\n","    while remaining > ndigits[1]+ndigits[0]:  # The remaining will be from min to max, i.e. one image\n","      this_num = random.randint(ndigits[0], ndigits[1])\n","      self.num_digits.append(this_num)\n","      remaining -= this_num\n","    self.num_digits.append(remaining)\n","\n","    self.num_digits = np.array(self.num_digits)\n","    self.start_digit = self.num_digits.cumsum() - self.num_digits[0]\n","\n","  def __len__(self):\n","    return len(self.num_digits)\n","\n","  def __getitem__(self, idx):\n","    sample = self.image_template.detach().clone()\n","    target = self.image_template.detach().clone()\n","\n","    for i in range(self.num_digits[idx]):\n","      digit, cls = self.mnist[(self.start_digit[idx]+i)%self.length]\n","      mask = digit>0\n","\n","      valid = False\n","      while not valid:\n","        y = random.randint(0, sample.size(1)-digit.size(1))\n","        x = random.randint(0, sample.size(2)-digit.size(2))\n","        valid = (mask*sample[:, y:y+digit.size(1), x:x+digit.size(2)]>0).sum() < self.max_iou*mask.sum()\n","\n","      sample[:,y:y+digit.size(1), x:x+digit.size(2)][mask] = digit[mask]\n","      target[:,y:y+digit.size(1), x:x+digit.size(2)][mask] = cls+1\n","\n","      if self.transform is not None:\n","        sample = self.transform(sample)\n","      if self.target_transform is not None:\n","        target = self.target_transform(target)\n","\n","    return (sample, target)"],"metadata":{"id":"eLLzcPrxHq1k","executionInfo":{"status":"ok","timestamp":1714222025506,"user_tz":-60,"elapsed":1102,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","\n","trans_train = v2.Compose([\n","    v2.ToImage(),\n","    v2.ToDtype(torch.float32, scale=True), # transform.ToTensor()\n","    v2.Normalize(mean=[0.5,], std=[0.5,])\n","    ])\n","\n","train_dataset = SegmentationMNIST(os.getcwd(), train=True, image_size=(256, 256), transform = trans_train)\n","test_dataset  = SegmentationMNIST(os.getcwd(), train=False, image_size=(256, 256), transform = trans_train)\n","\n","print('Train Size: ', train_dataset.__len__())\n","print('Test size: ', test_dataset.__len__())"],"metadata":{"id":"yPu6zVNOH40H","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222034110,"user_tz":-60,"elapsed":4441,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"774c82ae-c7a3-44dd-e9d3-21b7cd1042d4"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n"]},{"output_type":"stream","name":"stdout","text":["Train Size:  9985\n","Test size:  1676\n"]}]},{"cell_type":"code","source":["for sample, label in train_dataset:\n","  print(sample.shape)\n","  print(label.shape)\n","  break"],"metadata":{"id":"3QZgCtoBH-U9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222039577,"user_tz":-60,"elapsed":5,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"e6883255-099a-4694-a81f-77117e613b47"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([1, 256, 256])\n","torch.Size([1, 256, 256])\n"]}]},{"cell_type":"code","source":["rows = 2\n","cols = 8\n","\n","size_each = 2\n","fig, axes = plt.subplots(rows*2, cols,figsize =(cols*size_each, rows*2*size_each) )\n","\n","for ax in axes.reshape(-1):\n","  ax.axis('off')\n","\n","counter = 0\n","for r in range(rows):\n","  for c in range(cols):\n","    sample, label = train_dataset[counter]\n","    counter += 1\n","    axes[r*2][c].imshow(sample[0,...], cmap='gray')\n","    axes[r*2+1][c].imshow(label[0,...])"],"metadata":{"id":"-ZM1vfCYM4HU","colab":{"base_uri":"https://localhost:8080/","height":651},"executionInfo":{"status":"ok","timestamp":1714222045407,"user_tz":-60,"elapsed":2561,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"0dca0a04-344e-47d6-a9b4-d78ae21a3cff"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 32 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","VALIDATION_FRACTION = 0.2\n","n_sample = train_dataset.__len__()\n","\n","if VALIDATION_FRACTION is not None:\n","  num = int(VALIDATION_FRACTION * n_sample )\n","\n","  train_indices = torch.arange(0, n_sample  - num)\n","  valid_indices = torch.arange(n_sample  - num, n_sample )\n","\n","  indices = torch.arange(0, n_sample)\n","  shuffled_tensor = indices[torch.randperm(indices.size(0))]\n","\n","  train_indices = shuffled_tensor[train_indices]\n","  valid_indices = shuffled_tensor[valid_indices]\n","\n","  train_sampler = SubsetRandomSampler(train_indices)\n","  valid_sampler = SubsetRandomSampler(valid_indices)\n","\n","  print('Train size: ', len(train_sampler))\n","  print('Valid size: ', len(valid_sampler))"],"metadata":{"id":"E8oUtD-Vn1Bg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222049791,"user_tz":-60,"elapsed":378,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"f7a55559-e508-4cd8-ec47-84836068bf77"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n"]},{"output_type":"stream","name":"stdout","text":["Train size:  7988\n","Valid size:  1997\n"]}]},{"cell_type":"code","source":["BATCH_SIZE = 32\n","NUM_WORKERS = 8\n","\n","train_loader = DataLoader(dataset=train_dataset,\n","                          batch_size= BATCH_SIZE,\n","                          num_workers=NUM_WORKERS,\n","                          drop_last=True,\n","                          sampler=train_sampler)\n","\n","valid_loader = DataLoader(dataset=train_dataset,\n","                          batch_size=BATCH_SIZE,\n","                          num_workers=NUM_WORKERS,\n","                          sampler=valid_sampler,\n","                          shuffle=False)\n","\n","test_loader = DataLoader(dataset=test_dataset,\n","                          batch_size=1,\n","                          num_workers=NUM_WORKERS,\n","                          shuffle=False)\n","\n","print(\"Size of Train DataLoader:\", len(train_loader))\n","print(\"Size of Valid DataLoader:\", len(valid_loader))\n","print(\"Size of Test DataLoader:\", len(test_loader))"],"metadata":{"id":"NpEjNH7Nosjc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222052375,"user_tz":-60,"elapsed":2,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"e7b00a02-ae8b-4938-ca3b-25579c9ac1cc"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Size of Train DataLoader: 249\n","Size of Valid DataLoader: 63\n","Size of Test DataLoader: 1676\n"]}]},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","\n","for batch in train_loader:\n","    batch = batch.copy()\n","    images, labels = batch.copy()\n","    print('Image batch dimensions:', images.shape)\n","    print('Image label dimensions:', labels.shape)\n","    break\n","\n","fig, ax = plt.subplots(1, 4)\n","\n","for i in range(4):\n","  img = transforms.ToPILImage()(images[i])\n","  ax[i].imshow(img)\n","\n","plt.tight_layout()"],"metadata":{"id":"BHPxbtYC710z","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1714222057347,"user_tz":-60,"elapsed":1425,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"c23bd832-dfa9-4d4f-a53c-0e941a11a90f"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","  self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["Image batch dimensions: torch.Size([32, 1, 256, 256])\n","Image label dimensions: torch.Size([32, 1, 256, 256])\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["class UnetModel(pl.LightningModule):\n","    def __init__(self, model, num_classes, optimizer_name, optimizer_hparams):\n","        super().__init__()\n","        self.model = model\n","\n","        self.criterion =  smp.losses.DiceLoss(mode='multiclass', from_logits=True)\n","        self.num_classes = num_classes\n","        self.save_hyperparameters(ignore=['model'])\n","\n","    def forward(self, x):\n","        return self.model(x)\n","\n","    def _shared_step(self, batch):\n","        image, mask = batch\n","        out = self.forward(image)\n","        loss = self.criterion(out, mask.long())\n","\n","        tp, fp, fn, tn = smp.metrics.get_stats(torch.argmax(out, 1).unsqueeze(1), mask.long(), mode='multiclass', num_classes = self.num_classes)\n","\n","        # Metrics\n","        per_image_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro-imagewise\")\n","        image_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n","        accuracy = smp.metrics.balanced_accuracy(tp, fp, fn, tn, reduction=\"macro\")\n","\n","        return  {\"loss\": loss, \"per_image_iou\": per_image_iou, \"image_iou\": image_iou, \"accuracy\": accuracy}\n","\n","    def training_step(self, batch, batch_idx):\n","        outputs = self._shared_step(batch)\n","        loss = outputs['loss']\n","        per_image_iou = outputs['per_image_iou']\n","        image_iou = outputs['image_iou']\n","        accuracy = outputs['accuracy']\n","        # Metrics\n","        metrics = {'train_loss': loss, 'train_per_image_iou': per_image_iou, 'train_image_iou': image_iou, 'train_accuracy': accuracy}\n","\n","        self.log_dict(metrics, on_epoch=True, on_step=False, prog_bar=True)\n","        return loss # this is passed to the optimzer for training\n","\n","    def validation_step(self, batch, batch_idx):\n","        outputs = self._shared_step(batch)\n","        loss = outputs['loss']\n","        per_image_iou = outputs['per_image_iou']\n","        image_iou = outputs['image_iou']\n","        accuracy = outputs['accuracy']\n","        metrics = {'valid_loss': loss, 'valid_per_image_iou': per_image_iou, 'valid_image_iou': image_iou, 'valid_accuracy': accuracy}\n","        self.log_dict(metrics, on_epoch=True, on_step=False,  prog_bar=True)\n","\n","    def test_step(self, batch, batch_idx):\n","        outputs = self._shared_step(batch)\n","        loss = outputs['loss']\n","        per_image_iou = outputs['per_image_iou']\n","        image_iou = outputs['image_iou']\n","        accuracy = outputs['accuracy']\n","        metrics = {'test_loss': loss, 'test_per_image_iou': per_image_iou, 'test_image_iou': image_iou, 'test_accuracy': accuracy}\n","        self.log_dict(metrics, on_epoch=True, on_step=False, prog_bar=True)\n","\n","    def configure_optimizers(self):\n","      # We will support Adam or SGD as optimizers.\n","      if self.hparams.optimizer_name == \"Adam\":\n","          optimizer = torch.optim.AdamW(self.parameters(), **self.hparams.optimizer_hparams)\n","      elif self.hparams.optimizer_name == \"SGD\":\n","          optimizer = torch.optim.SGD(self.parameters(), **self.hparams.optimizer_hparams)\n","      else:\n","          assert False, f'Unknown optimizer: \"{self.hparams.optimizer_name}\"'\n","\n","      # scheduler = torch.optim.swa_utils.SWALR(optimizer, anneal_strategy=\"cos\", anneal_epochs=5, swa_lr=0.002)\n","      scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=3)\n","      return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler, \"monitor\": \"valid_loss\"}"],"metadata":{"id":"9aa7BULx8JEA","executionInfo":{"status":"ok","timestamp":1714222059858,"user_tz":-60,"elapsed":8,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}}},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":["## Model Encoder"],"metadata":{"id":"JkKi_Iw-sKF3"}},{"cell_type":"code","source":["arch = 'unet'\n","enc_name = 'efficientnet-b5'\n","classes = 11\n","\n","model_encoder = smp.create_model(arch,\n","                         encoder_name = enc_name,\n","                         encoder_weights = \"imagenet\",\n","                         in_channels = 1,\n","                         classes = classes).to(device)\n","\n","cbs = pl.callbacks.ModelCheckpoint(dirpath = f'./checkpoints_{arch}',\n","                                   filename = arch,\n","                                   verbose = True,\n","                                   monitor = 'valid_loss',\n","                                   mode = 'min')\n","\n","logger_csv = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"],"metadata":{"id":"qvpUKroM8fFQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222068436,"user_tz":-60,"elapsed":4498,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"90d2bcf8-35c3-4b7c-89df-48534b117b16"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b5-b6417697.pth\n","100%|██████████| 117M/117M [00:01<00:00, 68.1MB/s]\n"]}]},{"cell_type":"code","source":["input_size =  (1,256, 256)\n","# summary(model, input_size, device=\"cpu\")\n","summary(model_encoder, input_size)"],"metadata":{"id":"uWGbpCywVGWs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222073194,"user_tz":-60,"elapsed":1713,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"099a5a2e-2b6d-4a14-d0cd-e84c6c03a9de"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","         ZeroPad2d-1          [-1, 1, 257, 257]               0\n","Conv2dStaticSamePadding-2         [-1, 48, 128, 128]             432\n","       BatchNorm2d-3         [-1, 48, 128, 128]              96\n","MemoryEfficientSwish-4         [-1, 48, 128, 128]               0\n","         ZeroPad2d-5         [-1, 48, 130, 130]               0\n","Conv2dStaticSamePadding-6         [-1, 48, 128, 128]             432\n","       BatchNorm2d-7         [-1, 48, 128, 128]              96\n","MemoryEfficientSwish-8         [-1, 48, 128, 128]               0\n","          Identity-9             [-1, 48, 1, 1]               0\n","Conv2dStaticSamePadding-10             [-1, 12, 1, 1]             588\n","MemoryEfficientSwish-11             [-1, 12, 1, 1]               0\n","         Identity-12             [-1, 12, 1, 1]               0\n","Conv2dStaticSamePadding-13             [-1, 48, 1, 1]             624\n","         Identity-14         [-1, 48, 128, 128]               0\n","Conv2dStaticSamePadding-15         [-1, 24, 128, 128]           1,152\n","      BatchNorm2d-16         [-1, 24, 128, 128]              48\n","      MBConvBlock-17         [-1, 24, 128, 128]               0\n","        ZeroPad2d-18         [-1, 24, 130, 130]               0\n","Conv2dStaticSamePadding-19         [-1, 24, 128, 128]             216\n","      BatchNorm2d-20         [-1, 24, 128, 128]              48\n","MemoryEfficientSwish-21         [-1, 24, 128, 128]               0\n","         Identity-22             [-1, 24, 1, 1]               0\n","Conv2dStaticSamePadding-23              [-1, 6, 1, 1]             150\n","MemoryEfficientSwish-24              [-1, 6, 1, 1]               0\n","         Identity-25              [-1, 6, 1, 1]               0\n","Conv2dStaticSamePadding-26             [-1, 24, 1, 1]             168\n","         Identity-27         [-1, 24, 128, 128]               0\n","Conv2dStaticSamePadding-28         [-1, 24, 128, 128]             576\n","      BatchNorm2d-29         [-1, 24, 128, 128]              48\n","      MBConvBlock-30         [-1, 24, 128, 128]               0\n","        ZeroPad2d-31         [-1, 24, 130, 130]               0\n","Conv2dStaticSamePadding-32         [-1, 24, 128, 128]             216\n","      BatchNorm2d-33         [-1, 24, 128, 128]              48\n","MemoryEfficientSwish-34         [-1, 24, 128, 128]               0\n","         Identity-35             [-1, 24, 1, 1]               0\n","Conv2dStaticSamePadding-36              [-1, 6, 1, 1]             150\n","MemoryEfficientSwish-37              [-1, 6, 1, 1]               0\n","         Identity-38              [-1, 6, 1, 1]               0\n","Conv2dStaticSamePadding-39             [-1, 24, 1, 1]             168\n","         Identity-40         [-1, 24, 128, 128]               0\n","Conv2dStaticSamePadding-41         [-1, 24, 128, 128]             576\n","      BatchNorm2d-42         [-1, 24, 128, 128]              48\n","      MBConvBlock-43         [-1, 24, 128, 128]               0\n","         Identity-44         [-1, 24, 128, 128]               0\n","Conv2dStaticSamePadding-45        [-1, 144, 128, 128]           3,456\n","      BatchNorm2d-46        [-1, 144, 128, 128]             288\n","MemoryEfficientSwish-47        [-1, 144, 128, 128]               0\n","        ZeroPad2d-48        [-1, 144, 129, 129]               0\n","Conv2dStaticSamePadding-49          [-1, 144, 64, 64]           1,296\n","      BatchNorm2d-50          [-1, 144, 64, 64]             288\n","MemoryEfficientSwish-51          [-1, 144, 64, 64]               0\n","         Identity-52            [-1, 144, 1, 1]               0\n","Conv2dStaticSamePadding-53              [-1, 6, 1, 1]             870\n","MemoryEfficientSwish-54              [-1, 6, 1, 1]               0\n","         Identity-55              [-1, 6, 1, 1]               0\n","Conv2dStaticSamePadding-56            [-1, 144, 1, 1]           1,008\n","         Identity-57          [-1, 144, 64, 64]               0\n","Conv2dStaticSamePadding-58           [-1, 40, 64, 64]           5,760\n","      BatchNorm2d-59           [-1, 40, 64, 64]              80\n","      MBConvBlock-60           [-1, 40, 64, 64]               0\n","         Identity-61           [-1, 40, 64, 64]               0\n","Conv2dStaticSamePadding-62          [-1, 240, 64, 64]           9,600\n","      BatchNorm2d-63          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-64          [-1, 240, 64, 64]               0\n","        ZeroPad2d-65          [-1, 240, 66, 66]               0\n","Conv2dStaticSamePadding-66          [-1, 240, 64, 64]           2,160\n","      BatchNorm2d-67          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-68          [-1, 240, 64, 64]               0\n","         Identity-69            [-1, 240, 1, 1]               0\n","Conv2dStaticSamePadding-70             [-1, 10, 1, 1]           2,410\n","MemoryEfficientSwish-71             [-1, 10, 1, 1]               0\n","         Identity-72             [-1, 10, 1, 1]               0\n","Conv2dStaticSamePadding-73            [-1, 240, 1, 1]           2,640\n","         Identity-74          [-1, 240, 64, 64]               0\n","Conv2dStaticSamePadding-75           [-1, 40, 64, 64]           9,600\n","      BatchNorm2d-76           [-1, 40, 64, 64]              80\n","      MBConvBlock-77           [-1, 40, 64, 64]               0\n","         Identity-78           [-1, 40, 64, 64]               0\n","Conv2dStaticSamePadding-79          [-1, 240, 64, 64]           9,600\n","      BatchNorm2d-80          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-81          [-1, 240, 64, 64]               0\n","        ZeroPad2d-82          [-1, 240, 66, 66]               0\n","Conv2dStaticSamePadding-83          [-1, 240, 64, 64]           2,160\n","      BatchNorm2d-84          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-85          [-1, 240, 64, 64]               0\n","         Identity-86            [-1, 240, 1, 1]               0\n","Conv2dStaticSamePadding-87             [-1, 10, 1, 1]           2,410\n","MemoryEfficientSwish-88             [-1, 10, 1, 1]               0\n","         Identity-89             [-1, 10, 1, 1]               0\n","Conv2dStaticSamePadding-90            [-1, 240, 1, 1]           2,640\n","         Identity-91          [-1, 240, 64, 64]               0\n","Conv2dStaticSamePadding-92           [-1, 40, 64, 64]           9,600\n","      BatchNorm2d-93           [-1, 40, 64, 64]              80\n","      MBConvBlock-94           [-1, 40, 64, 64]               0\n","         Identity-95           [-1, 40, 64, 64]               0\n","Conv2dStaticSamePadding-96          [-1, 240, 64, 64]           9,600\n","      BatchNorm2d-97          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-98          [-1, 240, 64, 64]               0\n","        ZeroPad2d-99          [-1, 240, 66, 66]               0\n","Conv2dStaticSamePadding-100          [-1, 240, 64, 64]           2,160\n","     BatchNorm2d-101          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-102          [-1, 240, 64, 64]               0\n","        Identity-103            [-1, 240, 1, 1]               0\n","Conv2dStaticSamePadding-104             [-1, 10, 1, 1]           2,410\n","MemoryEfficientSwish-105             [-1, 10, 1, 1]               0\n","        Identity-106             [-1, 10, 1, 1]               0\n","Conv2dStaticSamePadding-107            [-1, 240, 1, 1]           2,640\n","        Identity-108          [-1, 240, 64, 64]               0\n","Conv2dStaticSamePadding-109           [-1, 40, 64, 64]           9,600\n","     BatchNorm2d-110           [-1, 40, 64, 64]              80\n","     MBConvBlock-111           [-1, 40, 64, 64]               0\n","        Identity-112           [-1, 40, 64, 64]               0\n","Conv2dStaticSamePadding-113          [-1, 240, 64, 64]           9,600\n","     BatchNorm2d-114          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-115          [-1, 240, 64, 64]               0\n","       ZeroPad2d-116          [-1, 240, 66, 66]               0\n","Conv2dStaticSamePadding-117          [-1, 240, 64, 64]           2,160\n","     BatchNorm2d-118          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-119          [-1, 240, 64, 64]               0\n","        Identity-120            [-1, 240, 1, 1]               0\n","Conv2dStaticSamePadding-121             [-1, 10, 1, 1]           2,410\n","MemoryEfficientSwish-122             [-1, 10, 1, 1]               0\n","        Identity-123             [-1, 10, 1, 1]               0\n","Conv2dStaticSamePadding-124            [-1, 240, 1, 1]           2,640\n","        Identity-125          [-1, 240, 64, 64]               0\n","Conv2dStaticSamePadding-126           [-1, 40, 64, 64]           9,600\n","     BatchNorm2d-127           [-1, 40, 64, 64]              80\n","     MBConvBlock-128           [-1, 40, 64, 64]               0\n","        Identity-129           [-1, 40, 64, 64]               0\n","Conv2dStaticSamePadding-130          [-1, 240, 64, 64]           9,600\n","     BatchNorm2d-131          [-1, 240, 64, 64]             480\n","MemoryEfficientSwish-132          [-1, 240, 64, 64]               0\n","       ZeroPad2d-133          [-1, 240, 67, 67]               0\n","Conv2dStaticSamePadding-134          [-1, 240, 32, 32]           6,000\n","     BatchNorm2d-135          [-1, 240, 32, 32]             480\n","MemoryEfficientSwish-136          [-1, 240, 32, 32]               0\n","        Identity-137            [-1, 240, 1, 1]               0\n","Conv2dStaticSamePadding-138             [-1, 10, 1, 1]           2,410\n","MemoryEfficientSwish-139             [-1, 10, 1, 1]               0\n","        Identity-140             [-1, 10, 1, 1]               0\n","Conv2dStaticSamePadding-141            [-1, 240, 1, 1]           2,640\n","        Identity-142          [-1, 240, 32, 32]               0\n","Conv2dStaticSamePadding-143           [-1, 64, 32, 32]          15,360\n","     BatchNorm2d-144           [-1, 64, 32, 32]             128\n","     MBConvBlock-145           [-1, 64, 32, 32]               0\n","        Identity-146           [-1, 64, 32, 32]               0\n","Conv2dStaticSamePadding-147          [-1, 384, 32, 32]          24,576\n","     BatchNorm2d-148          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-149          [-1, 384, 32, 32]               0\n","       ZeroPad2d-150          [-1, 384, 36, 36]               0\n","Conv2dStaticSamePadding-151          [-1, 384, 32, 32]           9,600\n","     BatchNorm2d-152          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-153          [-1, 384, 32, 32]               0\n","        Identity-154            [-1, 384, 1, 1]               0\n","Conv2dStaticSamePadding-155             [-1, 16, 1, 1]           6,160\n","MemoryEfficientSwish-156             [-1, 16, 1, 1]               0\n","        Identity-157             [-1, 16, 1, 1]               0\n","Conv2dStaticSamePadding-158            [-1, 384, 1, 1]           6,528\n","        Identity-159          [-1, 384, 32, 32]               0\n","Conv2dStaticSamePadding-160           [-1, 64, 32, 32]          24,576\n","     BatchNorm2d-161           [-1, 64, 32, 32]             128\n","     MBConvBlock-162           [-1, 64, 32, 32]               0\n","        Identity-163           [-1, 64, 32, 32]               0\n","Conv2dStaticSamePadding-164          [-1, 384, 32, 32]          24,576\n","     BatchNorm2d-165          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-166          [-1, 384, 32, 32]               0\n","       ZeroPad2d-167          [-1, 384, 36, 36]               0\n","Conv2dStaticSamePadding-168          [-1, 384, 32, 32]           9,600\n","     BatchNorm2d-169          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-170          [-1, 384, 32, 32]               0\n","        Identity-171            [-1, 384, 1, 1]               0\n","Conv2dStaticSamePadding-172             [-1, 16, 1, 1]           6,160\n","MemoryEfficientSwish-173             [-1, 16, 1, 1]               0\n","        Identity-174             [-1, 16, 1, 1]               0\n","Conv2dStaticSamePadding-175            [-1, 384, 1, 1]           6,528\n","        Identity-176          [-1, 384, 32, 32]               0\n","Conv2dStaticSamePadding-177           [-1, 64, 32, 32]          24,576\n","     BatchNorm2d-178           [-1, 64, 32, 32]             128\n","     MBConvBlock-179           [-1, 64, 32, 32]               0\n","        Identity-180           [-1, 64, 32, 32]               0\n","Conv2dStaticSamePadding-181          [-1, 384, 32, 32]          24,576\n","     BatchNorm2d-182          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-183          [-1, 384, 32, 32]               0\n","       ZeroPad2d-184          [-1, 384, 36, 36]               0\n","Conv2dStaticSamePadding-185          [-1, 384, 32, 32]           9,600\n","     BatchNorm2d-186          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-187          [-1, 384, 32, 32]               0\n","        Identity-188            [-1, 384, 1, 1]               0\n","Conv2dStaticSamePadding-189             [-1, 16, 1, 1]           6,160\n","MemoryEfficientSwish-190             [-1, 16, 1, 1]               0\n","        Identity-191             [-1, 16, 1, 1]               0\n","Conv2dStaticSamePadding-192            [-1, 384, 1, 1]           6,528\n","        Identity-193          [-1, 384, 32, 32]               0\n","Conv2dStaticSamePadding-194           [-1, 64, 32, 32]          24,576\n","     BatchNorm2d-195           [-1, 64, 32, 32]             128\n","     MBConvBlock-196           [-1, 64, 32, 32]               0\n","        Identity-197           [-1, 64, 32, 32]               0\n","Conv2dStaticSamePadding-198          [-1, 384, 32, 32]          24,576\n","     BatchNorm2d-199          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-200          [-1, 384, 32, 32]               0\n","       ZeroPad2d-201          [-1, 384, 36, 36]               0\n","Conv2dStaticSamePadding-202          [-1, 384, 32, 32]           9,600\n","     BatchNorm2d-203          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-204          [-1, 384, 32, 32]               0\n","        Identity-205            [-1, 384, 1, 1]               0\n","Conv2dStaticSamePadding-206             [-1, 16, 1, 1]           6,160\n","MemoryEfficientSwish-207             [-1, 16, 1, 1]               0\n","        Identity-208             [-1, 16, 1, 1]               0\n","Conv2dStaticSamePadding-209            [-1, 384, 1, 1]           6,528\n","        Identity-210          [-1, 384, 32, 32]               0\n","Conv2dStaticSamePadding-211           [-1, 64, 32, 32]          24,576\n","     BatchNorm2d-212           [-1, 64, 32, 32]             128\n","     MBConvBlock-213           [-1, 64, 32, 32]               0\n","        Identity-214           [-1, 64, 32, 32]               0\n","Conv2dStaticSamePadding-215          [-1, 384, 32, 32]          24,576\n","     BatchNorm2d-216          [-1, 384, 32, 32]             768\n","MemoryEfficientSwish-217          [-1, 384, 32, 32]               0\n","       ZeroPad2d-218          [-1, 384, 34, 34]               0\n","Conv2dStaticSamePadding-219          [-1, 384, 16, 16]           3,456\n","     BatchNorm2d-220          [-1, 384, 16, 16]             768\n","MemoryEfficientSwish-221          [-1, 384, 16, 16]               0\n","        Identity-222            [-1, 384, 1, 1]               0\n","Conv2dStaticSamePadding-223             [-1, 16, 1, 1]           6,160\n","MemoryEfficientSwish-224             [-1, 16, 1, 1]               0\n","        Identity-225             [-1, 16, 1, 1]               0\n","Conv2dStaticSamePadding-226            [-1, 384, 1, 1]           6,528\n","        Identity-227          [-1, 384, 16, 16]               0\n","Conv2dStaticSamePadding-228          [-1, 128, 16, 16]          49,152\n","     BatchNorm2d-229          [-1, 128, 16, 16]             256\n","     MBConvBlock-230          [-1, 128, 16, 16]               0\n","        Identity-231          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-232          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-233          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-234          [-1, 768, 16, 16]               0\n","       ZeroPad2d-235          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-236          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-237          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-238          [-1, 768, 16, 16]               0\n","        Identity-239            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-240             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-241             [-1, 32, 1, 1]               0\n","        Identity-242             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-243            [-1, 768, 1, 1]          25,344\n","        Identity-244          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-245          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-246          [-1, 128, 16, 16]             256\n","     MBConvBlock-247          [-1, 128, 16, 16]               0\n","        Identity-248          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-249          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-250          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-251          [-1, 768, 16, 16]               0\n","       ZeroPad2d-252          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-253          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-254          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-255          [-1, 768, 16, 16]               0\n","        Identity-256            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-257             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-258             [-1, 32, 1, 1]               0\n","        Identity-259             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-260            [-1, 768, 1, 1]          25,344\n","        Identity-261          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-262          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-263          [-1, 128, 16, 16]             256\n","     MBConvBlock-264          [-1, 128, 16, 16]               0\n","        Identity-265          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-266          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-267          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-268          [-1, 768, 16, 16]               0\n","       ZeroPad2d-269          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-270          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-271          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-272          [-1, 768, 16, 16]               0\n","        Identity-273            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-274             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-275             [-1, 32, 1, 1]               0\n","        Identity-276             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-277            [-1, 768, 1, 1]          25,344\n","        Identity-278          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-279          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-280          [-1, 128, 16, 16]             256\n","     MBConvBlock-281          [-1, 128, 16, 16]               0\n","        Identity-282          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-283          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-284          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-285          [-1, 768, 16, 16]               0\n","       ZeroPad2d-286          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-287          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-288          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-289          [-1, 768, 16, 16]               0\n","        Identity-290            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-291             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-292             [-1, 32, 1, 1]               0\n","        Identity-293             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-294            [-1, 768, 1, 1]          25,344\n","        Identity-295          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-296          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-297          [-1, 128, 16, 16]             256\n","     MBConvBlock-298          [-1, 128, 16, 16]               0\n","        Identity-299          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-300          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-301          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-302          [-1, 768, 16, 16]               0\n","       ZeroPad2d-303          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-304          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-305          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-306          [-1, 768, 16, 16]               0\n","        Identity-307            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-308             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-309             [-1, 32, 1, 1]               0\n","        Identity-310             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-311            [-1, 768, 1, 1]          25,344\n","        Identity-312          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-313          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-314          [-1, 128, 16, 16]             256\n","     MBConvBlock-315          [-1, 128, 16, 16]               0\n","        Identity-316          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-317          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-318          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-319          [-1, 768, 16, 16]               0\n","       ZeroPad2d-320          [-1, 768, 18, 18]               0\n","Conv2dStaticSamePadding-321          [-1, 768, 16, 16]           6,912\n","     BatchNorm2d-322          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-323          [-1, 768, 16, 16]               0\n","        Identity-324            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-325             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-326             [-1, 32, 1, 1]               0\n","        Identity-327             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-328            [-1, 768, 1, 1]          25,344\n","        Identity-329          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-330          [-1, 128, 16, 16]          98,304\n","     BatchNorm2d-331          [-1, 128, 16, 16]             256\n","     MBConvBlock-332          [-1, 128, 16, 16]               0\n","        Identity-333          [-1, 128, 16, 16]               0\n","Conv2dStaticSamePadding-334          [-1, 768, 16, 16]          98,304\n","     BatchNorm2d-335          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-336          [-1, 768, 16, 16]               0\n","       ZeroPad2d-337          [-1, 768, 20, 20]               0\n","Conv2dStaticSamePadding-338          [-1, 768, 16, 16]          19,200\n","     BatchNorm2d-339          [-1, 768, 16, 16]           1,536\n","MemoryEfficientSwish-340          [-1, 768, 16, 16]               0\n","        Identity-341            [-1, 768, 1, 1]               0\n","Conv2dStaticSamePadding-342             [-1, 32, 1, 1]          24,608\n","MemoryEfficientSwish-343             [-1, 32, 1, 1]               0\n","        Identity-344             [-1, 32, 1, 1]               0\n","Conv2dStaticSamePadding-345            [-1, 768, 1, 1]          25,344\n","        Identity-346          [-1, 768, 16, 16]               0\n","Conv2dStaticSamePadding-347          [-1, 176, 16, 16]         135,168\n","     BatchNorm2d-348          [-1, 176, 16, 16]             352\n","     MBConvBlock-349          [-1, 176, 16, 16]               0\n","        Identity-350          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-351         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-352         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-353         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-354         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-355         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-356         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-357         [-1, 1056, 16, 16]               0\n","        Identity-358           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-359             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-360             [-1, 44, 1, 1]               0\n","        Identity-361             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-362           [-1, 1056, 1, 1]          47,520\n","        Identity-363         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-364          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-365          [-1, 176, 16, 16]             352\n","     MBConvBlock-366          [-1, 176, 16, 16]               0\n","        Identity-367          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-368         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-369         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-370         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-371         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-372         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-373         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-374         [-1, 1056, 16, 16]               0\n","        Identity-375           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-376             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-377             [-1, 44, 1, 1]               0\n","        Identity-378             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-379           [-1, 1056, 1, 1]          47,520\n","        Identity-380         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-381          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-382          [-1, 176, 16, 16]             352\n","     MBConvBlock-383          [-1, 176, 16, 16]               0\n","        Identity-384          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-385         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-386         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-387         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-388         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-389         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-390         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-391         [-1, 1056, 16, 16]               0\n","        Identity-392           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-393             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-394             [-1, 44, 1, 1]               0\n","        Identity-395             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-396           [-1, 1056, 1, 1]          47,520\n","        Identity-397         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-398          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-399          [-1, 176, 16, 16]             352\n","     MBConvBlock-400          [-1, 176, 16, 16]               0\n","        Identity-401          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-402         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-403         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-404         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-405         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-406         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-407         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-408         [-1, 1056, 16, 16]               0\n","        Identity-409           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-410             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-411             [-1, 44, 1, 1]               0\n","        Identity-412             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-413           [-1, 1056, 1, 1]          47,520\n","        Identity-414         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-415          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-416          [-1, 176, 16, 16]             352\n","     MBConvBlock-417          [-1, 176, 16, 16]               0\n","        Identity-418          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-419         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-420         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-421         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-422         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-423         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-424         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-425         [-1, 1056, 16, 16]               0\n","        Identity-426           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-427             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-428             [-1, 44, 1, 1]               0\n","        Identity-429             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-430           [-1, 1056, 1, 1]          47,520\n","        Identity-431         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-432          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-433          [-1, 176, 16, 16]             352\n","     MBConvBlock-434          [-1, 176, 16, 16]               0\n","        Identity-435          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-436         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-437         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-438         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-439         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-440         [-1, 1056, 16, 16]          26,400\n","     BatchNorm2d-441         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-442         [-1, 1056, 16, 16]               0\n","        Identity-443           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-444             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-445             [-1, 44, 1, 1]               0\n","        Identity-446             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-447           [-1, 1056, 1, 1]          47,520\n","        Identity-448         [-1, 1056, 16, 16]               0\n","Conv2dStaticSamePadding-449          [-1, 176, 16, 16]         185,856\n","     BatchNorm2d-450          [-1, 176, 16, 16]             352\n","     MBConvBlock-451          [-1, 176, 16, 16]               0\n","        Identity-452          [-1, 176, 16, 16]               0\n","Conv2dStaticSamePadding-453         [-1, 1056, 16, 16]         185,856\n","     BatchNorm2d-454         [-1, 1056, 16, 16]           2,112\n","MemoryEfficientSwish-455         [-1, 1056, 16, 16]               0\n","       ZeroPad2d-456         [-1, 1056, 20, 20]               0\n","Conv2dStaticSamePadding-457           [-1, 1056, 8, 8]          26,400\n","     BatchNorm2d-458           [-1, 1056, 8, 8]           2,112\n","MemoryEfficientSwish-459           [-1, 1056, 8, 8]               0\n","        Identity-460           [-1, 1056, 1, 1]               0\n","Conv2dStaticSamePadding-461             [-1, 44, 1, 1]          46,508\n","MemoryEfficientSwish-462             [-1, 44, 1, 1]               0\n","        Identity-463             [-1, 44, 1, 1]               0\n","Conv2dStaticSamePadding-464           [-1, 1056, 1, 1]          47,520\n","        Identity-465           [-1, 1056, 8, 8]               0\n","Conv2dStaticSamePadding-466            [-1, 304, 8, 8]         321,024\n","     BatchNorm2d-467            [-1, 304, 8, 8]             608\n","     MBConvBlock-468            [-1, 304, 8, 8]               0\n","        Identity-469            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-470           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-471           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-472           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-473         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-474           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-475           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-476           [-1, 1824, 8, 8]               0\n","        Identity-477           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-478             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-479             [-1, 76, 1, 1]               0\n","        Identity-480             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-481           [-1, 1824, 1, 1]         140,448\n","        Identity-482           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-483            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-484            [-1, 304, 8, 8]             608\n","     MBConvBlock-485            [-1, 304, 8, 8]               0\n","        Identity-486            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-487           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-488           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-489           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-490         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-491           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-492           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-493           [-1, 1824, 8, 8]               0\n","        Identity-494           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-495             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-496             [-1, 76, 1, 1]               0\n","        Identity-497             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-498           [-1, 1824, 1, 1]         140,448\n","        Identity-499           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-500            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-501            [-1, 304, 8, 8]             608\n","     MBConvBlock-502            [-1, 304, 8, 8]               0\n","        Identity-503            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-504           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-505           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-506           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-507         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-508           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-509           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-510           [-1, 1824, 8, 8]               0\n","        Identity-511           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-512             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-513             [-1, 76, 1, 1]               0\n","        Identity-514             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-515           [-1, 1824, 1, 1]         140,448\n","        Identity-516           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-517            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-518            [-1, 304, 8, 8]             608\n","     MBConvBlock-519            [-1, 304, 8, 8]               0\n","        Identity-520            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-521           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-522           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-523           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-524         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-525           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-526           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-527           [-1, 1824, 8, 8]               0\n","        Identity-528           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-529             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-530             [-1, 76, 1, 1]               0\n","        Identity-531             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-532           [-1, 1824, 1, 1]         140,448\n","        Identity-533           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-534            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-535            [-1, 304, 8, 8]             608\n","     MBConvBlock-536            [-1, 304, 8, 8]               0\n","        Identity-537            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-538           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-539           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-540           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-541         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-542           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-543           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-544           [-1, 1824, 8, 8]               0\n","        Identity-545           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-546             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-547             [-1, 76, 1, 1]               0\n","        Identity-548             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-549           [-1, 1824, 1, 1]         140,448\n","        Identity-550           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-551            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-552            [-1, 304, 8, 8]             608\n","     MBConvBlock-553            [-1, 304, 8, 8]               0\n","        Identity-554            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-555           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-556           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-557           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-558         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-559           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-560           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-561           [-1, 1824, 8, 8]               0\n","        Identity-562           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-563             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-564             [-1, 76, 1, 1]               0\n","        Identity-565             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-566           [-1, 1824, 1, 1]         140,448\n","        Identity-567           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-568            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-569            [-1, 304, 8, 8]             608\n","     MBConvBlock-570            [-1, 304, 8, 8]               0\n","        Identity-571            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-572           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-573           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-574           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-575         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-576           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-577           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-578           [-1, 1824, 8, 8]               0\n","        Identity-579           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-580             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-581             [-1, 76, 1, 1]               0\n","        Identity-582             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-583           [-1, 1824, 1, 1]         140,448\n","        Identity-584           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-585            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-586            [-1, 304, 8, 8]             608\n","     MBConvBlock-587            [-1, 304, 8, 8]               0\n","        Identity-588            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-589           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-590           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-591           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-592         [-1, 1824, 12, 12]               0\n","Conv2dStaticSamePadding-593           [-1, 1824, 8, 8]          45,600\n","     BatchNorm2d-594           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-595           [-1, 1824, 8, 8]               0\n","        Identity-596           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-597             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-598             [-1, 76, 1, 1]               0\n","        Identity-599             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-600           [-1, 1824, 1, 1]         140,448\n","        Identity-601           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-602            [-1, 304, 8, 8]         554,496\n","     BatchNorm2d-603            [-1, 304, 8, 8]             608\n","     MBConvBlock-604            [-1, 304, 8, 8]               0\n","        Identity-605            [-1, 304, 8, 8]               0\n","Conv2dStaticSamePadding-606           [-1, 1824, 8, 8]         554,496\n","     BatchNorm2d-607           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-608           [-1, 1824, 8, 8]               0\n","       ZeroPad2d-609         [-1, 1824, 10, 10]               0\n","Conv2dStaticSamePadding-610           [-1, 1824, 8, 8]          16,416\n","     BatchNorm2d-611           [-1, 1824, 8, 8]           3,648\n","MemoryEfficientSwish-612           [-1, 1824, 8, 8]               0\n","        Identity-613           [-1, 1824, 1, 1]               0\n","Conv2dStaticSamePadding-614             [-1, 76, 1, 1]         138,700\n","MemoryEfficientSwish-615             [-1, 76, 1, 1]               0\n","        Identity-616             [-1, 76, 1, 1]               0\n","Conv2dStaticSamePadding-617           [-1, 1824, 1, 1]         140,448\n","        Identity-618           [-1, 1824, 8, 8]               0\n","Conv2dStaticSamePadding-619            [-1, 512, 8, 8]         933,888\n","     BatchNorm2d-620            [-1, 512, 8, 8]           1,024\n","     MBConvBlock-621            [-1, 512, 8, 8]               0\n","        Identity-622            [-1, 512, 8, 8]               0\n","Conv2dStaticSamePadding-623           [-1, 3072, 8, 8]       1,572,864\n","     BatchNorm2d-624           [-1, 3072, 8, 8]           6,144\n","MemoryEfficientSwish-625           [-1, 3072, 8, 8]               0\n","       ZeroPad2d-626         [-1, 3072, 10, 10]               0\n","Conv2dStaticSamePadding-627           [-1, 3072, 8, 8]          27,648\n","     BatchNorm2d-628           [-1, 3072, 8, 8]           6,144\n","MemoryEfficientSwish-629           [-1, 3072, 8, 8]               0\n","        Identity-630           [-1, 3072, 1, 1]               0\n","Conv2dStaticSamePadding-631            [-1, 128, 1, 1]         393,344\n","MemoryEfficientSwish-632            [-1, 128, 1, 1]               0\n","        Identity-633            [-1, 128, 1, 1]               0\n","Conv2dStaticSamePadding-634           [-1, 3072, 1, 1]         396,288\n","        Identity-635           [-1, 3072, 8, 8]               0\n","Conv2dStaticSamePadding-636            [-1, 512, 8, 8]       1,572,864\n","     BatchNorm2d-637            [-1, 512, 8, 8]           1,024\n","     MBConvBlock-638            [-1, 512, 8, 8]               0\n","        Identity-639            [-1, 512, 8, 8]               0\n","Conv2dStaticSamePadding-640           [-1, 3072, 8, 8]       1,572,864\n","     BatchNorm2d-641           [-1, 3072, 8, 8]           6,144\n","MemoryEfficientSwish-642           [-1, 3072, 8, 8]               0\n","       ZeroPad2d-643         [-1, 3072, 10, 10]               0\n","Conv2dStaticSamePadding-644           [-1, 3072, 8, 8]          27,648\n","     BatchNorm2d-645           [-1, 3072, 8, 8]           6,144\n","MemoryEfficientSwish-646           [-1, 3072, 8, 8]               0\n","        Identity-647           [-1, 3072, 1, 1]               0\n","Conv2dStaticSamePadding-648            [-1, 128, 1, 1]         393,344\n","MemoryEfficientSwish-649            [-1, 128, 1, 1]               0\n","        Identity-650            [-1, 128, 1, 1]               0\n","Conv2dStaticSamePadding-651           [-1, 3072, 1, 1]         396,288\n","        Identity-652           [-1, 3072, 8, 8]               0\n","Conv2dStaticSamePadding-653            [-1, 512, 8, 8]       1,572,864\n","     BatchNorm2d-654            [-1, 512, 8, 8]           1,024\n","     MBConvBlock-655            [-1, 512, 8, 8]               0\n","EfficientNetEncoder-656  [[-1, 1, 256, 256], [-1, 48, 128, 128], [-1, 40, 64, 64], [-1, 64, 32, 32], [-1, 176, 16, 16], [-1, 512, 8, 8]]               0\n","        Identity-657            [-1, 512, 8, 8]               0\n","        Identity-658          [-1, 688, 16, 16]               0\n","       Attention-659          [-1, 688, 16, 16]               0\n","          Conv2d-660          [-1, 256, 16, 16]       1,585,152\n","     BatchNorm2d-661          [-1, 256, 16, 16]             512\n","            ReLU-662          [-1, 256, 16, 16]               0\n","          Conv2d-663          [-1, 256, 16, 16]         589,824\n","     BatchNorm2d-664          [-1, 256, 16, 16]             512\n","            ReLU-665          [-1, 256, 16, 16]               0\n","        Identity-666          [-1, 256, 16, 16]               0\n","       Attention-667          [-1, 256, 16, 16]               0\n","    DecoderBlock-668          [-1, 256, 16, 16]               0\n","        Identity-669          [-1, 320, 32, 32]               0\n","       Attention-670          [-1, 320, 32, 32]               0\n","          Conv2d-671          [-1, 128, 32, 32]         368,640\n","     BatchNorm2d-672          [-1, 128, 32, 32]             256\n","            ReLU-673          [-1, 128, 32, 32]               0\n","          Conv2d-674          [-1, 128, 32, 32]         147,456\n","     BatchNorm2d-675          [-1, 128, 32, 32]             256\n","            ReLU-676          [-1, 128, 32, 32]               0\n","        Identity-677          [-1, 128, 32, 32]               0\n","       Attention-678          [-1, 128, 32, 32]               0\n","    DecoderBlock-679          [-1, 128, 32, 32]               0\n","        Identity-680          [-1, 168, 64, 64]               0\n","       Attention-681          [-1, 168, 64, 64]               0\n","          Conv2d-682           [-1, 64, 64, 64]          96,768\n","     BatchNorm2d-683           [-1, 64, 64, 64]             128\n","            ReLU-684           [-1, 64, 64, 64]               0\n","          Conv2d-685           [-1, 64, 64, 64]          36,864\n","     BatchNorm2d-686           [-1, 64, 64, 64]             128\n","            ReLU-687           [-1, 64, 64, 64]               0\n","        Identity-688           [-1, 64, 64, 64]               0\n","       Attention-689           [-1, 64, 64, 64]               0\n","    DecoderBlock-690           [-1, 64, 64, 64]               0\n","        Identity-691        [-1, 112, 128, 128]               0\n","       Attention-692        [-1, 112, 128, 128]               0\n","          Conv2d-693         [-1, 32, 128, 128]          32,256\n","     BatchNorm2d-694         [-1, 32, 128, 128]              64\n","            ReLU-695         [-1, 32, 128, 128]               0\n","          Conv2d-696         [-1, 32, 128, 128]           9,216\n","     BatchNorm2d-697         [-1, 32, 128, 128]              64\n","            ReLU-698         [-1, 32, 128, 128]               0\n","        Identity-699         [-1, 32, 128, 128]               0\n","       Attention-700         [-1, 32, 128, 128]               0\n","    DecoderBlock-701         [-1, 32, 128, 128]               0\n","          Conv2d-702         [-1, 16, 256, 256]           4,608\n","     BatchNorm2d-703         [-1, 16, 256, 256]              32\n","            ReLU-704         [-1, 16, 256, 256]               0\n","          Conv2d-705         [-1, 16, 256, 256]           2,304\n","     BatchNorm2d-706         [-1, 16, 256, 256]              32\n","            ReLU-707         [-1, 16, 256, 256]               0\n","        Identity-708         [-1, 16, 256, 256]               0\n","       Attention-709         [-1, 16, 256, 256]               0\n","    DecoderBlock-710         [-1, 16, 256, 256]               0\n","     UnetDecoder-711         [-1, 16, 256, 256]               0\n","          Conv2d-712         [-1, 11, 256, 256]           1,595\n","        Identity-713         [-1, 11, 256, 256]               0\n","        Identity-714         [-1, 11, 256, 256]               0\n","      Activation-715         [-1, 11, 256, 256]               0\n","================================================================\n","Total params: 30,163,915\n","Trainable params: 30,163,915\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.25\n","Forward/backward pass size (MB): 1174.03\n","Params size (MB): 115.07\n","Estimated Total Size (MB): 1289.35\n","----------------------------------------------------------------\n"]}]},{"cell_type":"markdown","source":["## Run the model from checkpoint\n","### Run here only if you will train the model"],"metadata":{"id":"IAnixNa_sa3_"}},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","\n","optimizer_name = \"Adam\"\n","optimizer_hparams = {\"lr\": 0.0001, \"betas\": (0.9, 0.999), \"eps\": 1e-08, \"weight_decay\": 0.001}\n","num_classes = 11\n","\n","\n","\n","## model, num_classes, optimizer_name, optimizer_hparams\n","model_PL_wrapper = UnetModel.load_from_checkpoint(model = model_encoder,\n","                                                   num_classes = num_classes,\n","                                                   optimizer_name = optimizer_name,\n","                                                   optimizer_hparams = optimizer_hparams,\n","                                                   checkpoint_path=os.path.join('/content/gdrive/MyDrive/Colab Notebooks/TorchModel/checkpoints_unet/', 'unet.ckpt'))\n","\n","PERCENT_VALID_EXAMPLES = 1\n","EPOCHS = 30\n","LOG_EVERY_N_STEPS = 1\n","\n","trainer = pl.Trainer(\n","    limit_val_batches = PERCENT_VALID_EXAMPLES,\n","    max_epochs = EPOCHS,\n","    logger=logger_csv,\n","    callbacks = cbs,\n","    log_every_n_steps=LOG_EVERY_N_STEPS,\n","    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n","    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n","\n",")\n","\n","# trainer.fit(model_PL_wrapper, train_loader, valid_loader)"],"metadata":{"id":"FwpGbWOTsVCd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222087002,"user_tz":-60,"elapsed":7076,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"4de5ed64-f80a-4731-db36-39aa287b3ddc"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n","INFO: GPU available: False, used: False\n","INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n","INFO: TPU available: False, using: 0 TPU cores\n","INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO: IPU available: False, using: 0 IPUs\n","INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n","INFO: HPU available: False, using: 0 HPUs\n","INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO: `Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n","INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n"]}]},{"cell_type":"markdown","source":["## Run the model from the scratch\n","### Run here only if you will train the model"],"metadata":{"id":"Y2f_STTRtB3A"}},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","\n","optimizer_name = \"Adam\"\n","optimizer_hparams = {\"lr\": 0.0001, \"betas\": (0.9, 0.999), \"eps\": 1e-08, \"weight_decay\": 0.001}\n","num_classes = 11\n","\n","pl_model = UnetModel(model_encoder, num_classes, optimizer_name, optimizer_hparams)\n","\n","PERCENT_VALID_EXAMPLES = 1\n","EPOCHS = 30\n","LOG_EVERY_N_STEPS = 1\n","\n","trainer = pl.Trainer(\n","    limit_val_batches = PERCENT_VALID_EXAMPLES,\n","    max_epochs = EPOCHS,\n","    logger=logger_csv,\n","    callbacks = cbs,\n","    log_every_n_steps=LOG_EVERY_N_STEPS,\n","    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n","    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n","\n",")\n","\n","# trainer.fit(pl_model, train_loader, valid_loader)\n"],"metadata":{"id":"MnBfhsDP8jT3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222091644,"user_tz":-60,"elapsed":618,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"1e8572c9-13cb-4b93-9d50-130a2f10a5c3"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n","INFO: GPU available: False, used: False\n","INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n","INFO: TPU available: False, using: 0 TPU cores\n","INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO: IPU available: False, using: 0 IPUs\n","INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n","INFO: HPU available: False, using: 0 HPUs\n","INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO: `Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n","INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_val_batches=1)` was configured so 1 batch will be used.\n"]}]},{"cell_type":"markdown","source":["## Load the model from the checkpoint"],"metadata":{"id":"GqI-CrLvtK9Q"}},{"cell_type":"code","source":["# If you are working in GPU, please, you do not run: device = torch.device('cpu')\n","device = torch.device('cpu')\n","checkpoint_file = '/content/gdrive/MyDrive/Colab Notebooks/TorchModel/checkpoints_unet/unet.ckpt'\n","\n","pl.seed_everything(RANDOM_SEED)\n","\n","optimizer_name = \"Adam\"\n","optimizer_hparams = {\"lr\": 0.0001, \"betas\": (0.9, 0.999), \"eps\": 1e-08, \"weight_decay\": 0.001}\n","num_classes = 11\n","\n","ModelUnet = UnetModel(model_encoder, num_classes, optimizer_name, optimizer_hparams)\n","\n","checkpoint = torch.load(checkpoint_file, map_location=device)\n","ModelUnet.load_state_dict(checkpoint['state_dict'])"],"metadata":{"id":"abeZzJuWx8oY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222096289,"user_tz":-60,"elapsed":480,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"a091131a-f8ef-46f8-a600-d79f5774f6cf"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n"]},{"output_type":"execute_result","data":{"text/plain":["<All keys matched successfully>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["import pandas as pd\n","\n","def printdf(df):\n","  return display(pd.DataFrame(df))\n","\n","path = '/content/gdrive/MyDrive/Colab Notebooks/TorchModel/logs/my-model/version_0'\n","metrics = pd.read_csv(f\"{path}/metrics.csv\")\n","metrics\n","\n","aggreg_metrics = []\n","agg_col = \"epoch\"\n","for i, dfg in metrics.groupby(agg_col):\n","    agg = dict(dfg.mean())\n","    agg[agg_col] = i\n","    aggreg_metrics.append(agg)\n","\n","df_metrics = pd.DataFrame(aggreg_metrics)\n","\n","\n","df_metrics[[\"train_loss\", \"valid_loss\"]].plot(grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\n","df_metrics[[\"train_per_image_iou\", \"valid_per_image_iou\"]].plot(grid=True, legend=True, xlabel='Epoch', ylabel='per_image_iou')"],"metadata":{"id":"j3hzStZkSnPb","colab":{"base_uri":"https://localhost:8080/","height":899},"executionInfo":{"status":"ok","timestamp":1714222102280,"user_tz":-60,"elapsed":2553,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"3994a869-379b-4fe7-c786-843c11e821a2"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Epoch', ylabel='per_image_iou'>"]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## This cell must be run if it is the first time.\n","\n","### The results can called from the predict folder."],"metadata":{"id":"kf4iPAgnY0qm"}},{"cell_type":"code","source":["## It will take 10min more or less\n","pl.seed_everything(RANDOM_SEED)\n","\n","target_list = []\n","predict_list = []\n","prob_predict_list = []\n","\n","for idx, batch in enumerate(test_loader):\n","    images, targets = batch\n","    with torch.no_grad():\n","      model_encoder.eval()\n","      logits = model_encoder(images)\n","\n","    prob_predict = logits.sigmoid()\n","\n","    predict_prob0 = np.transpose(prob_predict[:,0,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob1 = np.transpose(prob_predict[:,1,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob2 = np.transpose(prob_predict[:,2,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob3 = np.transpose(prob_predict[:,3,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob4 = np.transpose(prob_predict[:,4,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob5 = np.transpose(prob_predict[:,5,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob6 = np.transpose(prob_predict[:,6,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob7 = np.transpose(prob_predict[:,7,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob8 = np.transpose(prob_predict[:,8,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob9 = np.transpose(prob_predict[:,9,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","    predict_prob10 = np.transpose(prob_predict[:,10,:,:].cpu().numpy(), (1, 2, 0)).flatten().reshape(-1, 1)\n","\n","    prob_predict_ = np.concatenate((predict_prob0, predict_prob1, predict_prob2, predict_prob3,\n","                                    predict_prob4, predict_prob5, predict_prob6, predict_prob7,\n","                                    predict_prob8, predict_prob9, predict_prob10), axis=1)\n","\n","    pr_masks = torch.argmax(logits, 1).unsqueeze(1)\n","\n","    target = np.transpose(targets[0].cpu().numpy(), (1, 2, 0)).astype(int)\n","    target_predict = np.transpose(pr_masks[0].cpu().numpy(), (1, 2, 0)).astype(int)\n","\n","    target_flatten = target.flatten().reshape(-1, 1)\n","    target_predict_flatten = target_predict.flatten().reshape(-1, 1)\n","\n","    target_list.append(target_flatten)\n","    predict_list.append(target_predict_flatten)\n","    prob_predict_list.append(prob_predict_)\n","\n","target_ = np.vstack(target_list)\n","predict_ = np.vstack(predict_list)\n","prob_predict_ = np.vstack(prob_predict_list)\n"],"metadata":{"id":"zLNMmt4zc7KN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714222445105,"user_tz":-60,"elapsed":337920,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"ea75a209-0a54-4977-9421-143bfa48fba5"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","  self.pid = os.fork()\n"]}]},{"cell_type":"markdown","source":["##  It will call the results saved from the previous cell in folder"],"metadata":{"id":"rWEFdg6oyzY8"}},{"cell_type":"code","source":["# %mkdir predict\n","\n","path = '/content/gdrive/MyDrive/Colab Notebooks/TorchModel//predict'\n","\n","# np.save(f'{path}/target.npy', target_)\n","# np.save(f'{path}/predict.npy', predict_)\n","# np.save(f'{path}/prob_predict.npy', prob_predict_)\n","\n","target = np.load(f'{path}/target.npy')\n","predict = np.load(f'{path}/predict.npy')\n","prob_predict = np.load(f'{path}/prob_predict.npy')\n","\n","print(target.shape)\n","print(predict.shape)\n","print(prob_predict.shape)"],"metadata":{"id":"Yw8aT2ONWP3p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714224713284,"user_tz":-60,"elapsed":39864,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"cb2da430-aef1-4eac-adeb-4d74460d0760"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["(109838336, 1)\n","(109838336, 1)\n","(109838336, 11)\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","\n","# Assuming target_flatten and target_pred_flatten are your actual and predicted labels respectively\n","conf_matrix = confusion_matrix(target, predict)\n","\n","print(\"Confusion Matrix:\")\n","printdf(conf_matrix)\n"],"metadata":{"id":"iqVzVB6E65jP","colab":{"base_uri":"https://localhost:8080/","height":412},"executionInfo":{"status":"ok","timestamp":1714223096247,"user_tz":-60,"elapsed":22464,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"bd73a71d-0879-487a-f3a4-7b942f566aea"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"]},{"output_type":"display_data","data":{"text/plain":["           0       1       2       3       4       5       6       7       8   \\\n","0   106035137    5490    3745   59259    3439    4263    2933    2827    3296   \n","1       12026  429731     309    1442     677     558     544    2105     106   \n","2        6745    1448  231693     790    1172    1941     236    1576    1338   \n","3       11440    2498     945  401204    3011    1653     398     813    3360   \n","4       12581     682     153    1341  391201     298    3098     721    1163   \n","5       12780     613    1181     926     178  330104     304    1073     604   \n","6       10399     822     377     345    4570     527  304903    2892     387   \n","7       10270    3379     923     596     805    1231    1146  350662     203   \n","8       13987     818    1182    3959    1612     947     520     301  311566   \n","9       10699    1163     416    1398     956    1566    1643    1560     677   \n","10      12853    1531     563    1657    1030    2058    1139     510    2228   \n","\n","        9       10  \n","0     3710    3705  \n","1     1435    2295  \n","2      735    1530  \n","3      920     507  \n","4     2170     687  \n","5     1666    5171  \n","6     2359     434  \n","7     1557     597  \n","8      671    3518  \n","9   383271    2495  \n","10    3240  343538  "],"text/html":["\n","  <div id=\"df-68aea61a-d07f-48fe-b12a-e2df4d8e0494\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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<td>12026</td>\n","      <td>429731</td>\n","      <td>309</td>\n","      <td>1442</td>\n","      <td>677</td>\n","      <td>558</td>\n","      <td>544</td>\n","      <td>2105</td>\n","      <td>106</td>\n","      <td>1435</td>\n","      <td>2295</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>6745</td>\n","      <td>1448</td>\n","      <td>231693</td>\n","      <td>790</td>\n","      <td>1172</td>\n","      <td>1941</td>\n","      <td>236</td>\n","      <td>1576</td>\n","      <td>1338</td>\n","      <td>735</td>\n","      <td>1530</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11440</td>\n","      <td>2498</td>\n","      <td>945</td>\n","      <td>401204</td>\n","      <td>3011</td>\n","      <td>1653</td>\n","      <td>398</td>\n","      <td>813</td>\n","      <td>3360</td>\n","      <td>920</td>\n","      <td>507</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>12581</td>\n","      <td>682</td>\n","      <td>153</td>\n","     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\"num_unique_values\": 11,\n        \"samples\": [\n          1073,\n          2827,\n          1560\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93544,\n        \"min\": 106,\n        \"max\": 311566,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          604,\n          3296,\n          677\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115008,\n        \"min\": 671,\n        \"max\": 383271,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1666,\n          3710,\n          383271\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 10,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102960,\n        \"min\": 434,\n        \"max\": 343538,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5171,\n          3705,\n          2495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","target_names = ['BackGround', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","print(classification_report(target, predict, target_names=target_names))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YDZQFUJsudmg","executionInfo":{"status":"ok","timestamp":1714224961746,"user_tz":-60,"elapsed":212587,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"f3967a6b-168d-482c-a562-505df2ea6323"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","  BackGround       1.00      1.00      1.00 106127804\n","           0       0.96      0.95      0.96    451228\n","           1       0.96      0.93      0.94    249204\n","           2       0.85      0.94      0.89    426749\n","           3       0.96      0.94      0.95    414095\n","           4       0.96      0.93      0.94    354600\n","           5       0.96      0.93      0.95    328015\n","           6       0.96      0.94      0.95    371369\n","           7       0.96      0.92      0.94    339081\n","           8       0.95      0.94      0.95    405844\n","           9       0.94      0.93      0.94    370347\n","\n","    accuracy                           1.00 109838336\n","   macro avg       0.95      0.94      0.95 109838336\n","weighted avg       1.00      1.00      1.00 109838336\n","\n"]}]},{"cell_type":"code","source":["from itertools import cycle\n","from sklearn.metrics import auc, roc_curve\n","from sklearn.metrics import RocCurveDisplay\n","from sklearn.preprocessing import LabelBinarizer\n","\n","label_binarizer = LabelBinarizer().fit(target)\n","target_onehot = label_binarizer.transform(target)\n","predict_onehot = label_binarizer.transform(predict)\n","\n","target_names = ['BackGround', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\", \"red\", \"green\", \"blue\", \"yellow\", \"purple\", \"pink\", \"orange\", \"cyan\"])\n","\n","print(prob_predict.shape)\n","print(target_onehot.shape)\n","\n","fig, ax = plt.subplots(figsize=(10, 10))\n","\n","n_classes = 11\n","for class_id, color in zip(range(n_classes), colors):\n","  RocCurveDisplay.from_predictions(\n","        target_onehot[:, class_id],\n","        prob_predict[:, class_id],\n","        name=f\"ROC curve for {target_names[class_id]}\",\n","        color=color,\n","        ax=ax\n","    )\n","\n","_ = ax.set(\n","    xlabel=\"False Positive Rate\",\n","    ylabel=\"True Positive Rate\",\n","    title=\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\",\n",")\n"],"metadata":{"id":"RVzx5_kmSem9","colab":{"base_uri":"https://localhost:8080/","height":930},"executionInfo":{"status":"ok","timestamp":1714223726678,"user_tz":-60,"elapsed":622464,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"a92f54d9-fab5-46d6-ea64-52520cf96219"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["(109838336, 11)\n","(109838336, 11)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["pl.seed_everything(RANDOM_SEED)\n","\n","test_dataloader = test_loader\n","\n","for idx, batch in enumerate(test_dataloader):\n","    images, targets = batch\n","    with torch.no_grad():\n","        model_encoder.eval()\n","        logits = model_encoder(images)\n","\n","    pr_masks = torch.argmax(logits, 1).unsqueeze(1)\n","\n","    img = np.transpose(images[0].cpu().numpy(), (1, 2, 0))\n","    target = np.transpose(targets[0].cpu().numpy(), (1, 2, 0)).astype(int)\n","    target_pred = np.transpose(pr_masks[0].cpu().numpy(), (1, 2, 0)).astype(int)\n","\n","    plt.figure(figsize=(15, 10))\n","\n","    plt.subplot(1, 3, 1)\n","    plt.imshow(img)\n","    plt.title(\"Image\")\n","    plt.axis(\"off\")\n","\n","    plt.subplot(1, 3, 2)\n","    plt.imshow(target)\n","    plt.title(\"Ground truth\")\n","    plt.axis(\"off\")\n","\n","    plt.subplot(1, 3, 3)\n","    plt.imshow(target_pred)\n","    plt.title(\"Prediction\")\n","    plt.axis(\"off\")\n","\n","    plt.show()\n","    if idx == 15:\n","     break\n"],"metadata":{"id":"-l6T6O4bIenV","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1714223751613,"user_tz":-60,"elapsed":12802,"user":{"displayName":"Bruno Soares de Castro","userId":"11507589105817192721"}},"outputId":"41628d81-33c1-4ff6-cf32-33361c0f0f66"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO: Seed set to 123\n","INFO:lightning.fabric.utilities.seed:Seed set to 123\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","  self.pid = os.fork()\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 3 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gQAk5ZZ1TkIzJVlYqe/A0/eKbNyoqp6hRz13yfOdE9AQAJa6xWbmGbf3ujkwYLzemZvfObfNXrG08LrPfJEniiwAAjCTO5f81NCnX2NjvYdQaAMVWMsGTjCTjZI2T18/H4ZJMpX7TOE+/X3SoTIcnmxu6aaNWRnETlZVVymR03QO/kC8jT39WVE7l1ihurBImoog8pV0uP/MJAIDdYGfsV+whAACKLFi+Ri6bCe381BoAQ6Fkgqcg4jR5TLPeNXaFZka3yu701jb6HXojM0dPr5+p2T/LyubS8tZuUX+TUjddPV+dR7Xrvfst1WibD6g6XUZb/ZxeSU/VgfGN8rQ9uPKMlad8ADXaBqqObS8AniKysooaT1ZGaZdTc5BRykm+o9cTAJSUTFbaXC9JCjpT/R4WmTxJLpnYvdfYGz1AAAD7rkxWbtPWXYZO1BoA+4KSCZ6cJ02paNIR5Ss1KZKT1fZ1zDn5Wpmr0LLO8WpqqNC45/4pJyknyZaXyx04Q0HUynlWLmLkrFH2nS167/QlOr3mJVXaiKyMOpyvzX6ZXu6YqtmxzUqYfPDkyynrfEWNJ89YlSmmYIdZV93L6yQpkFNWvlJO+u9t8/X//nWU5uife+vXBAAImfED5Vpatm+wnmx5stdxriIphX3nokxWpq0j3NcAAOx1xg/kt7Zu30CtAbAPK5ngSdZparJRB0brNcZu/9DNyVdrkNFz7Qfq5cbJss0Rmej2u92ZuslaekmZbGVWiWRGyXhW5bGM3lu7WmdVv6Cj4k62K0hq8I1WZMdpUdMUnVr1qvyuQKncZhTtmv3UHT719fGedb6yzlfaBdoaxPWb379Tc77yXKi/FgBAEexwhdgmtvfFGLTuJdm7c8XZOampVbmtW3fvtQEA+zZqDYBhonSCpz4EcuoIslqTi+qxzQdo9cYxchGn5d86XEHMSUZy0UCJ0Z06dNJ6HVW9WoeXrdasaItG21jP0jhJagxSWpat1T/apmv5plpd8vplmvyEUXxbTnfcdbNks4rLV9R58oyRld1hHEFX6BQoK6eUc/qv8z6qaS++QHM+ACgxLhGTt/+soTnZ1ka51laZmXWDHIRTsGKNXDo9NOMAAOxTqDUAhpOSDp52VB7NKJbIKV1p5SKBpk9oUG1Zm8bG2jUp3qQDy9ZrWqRB472sRntxRXaas/Tnzkl6vPFgvVQ/SdnmuEzaKtLpK9KRVVZG7YFTxvjy5MszRkmz/fkp5yvVdRe7R9rn6I5v/5vGLlksnw9pACg9Q9kTwwVyzsk4V3jOnR/vKJuTNm2Vy4TXbBYAUGRDUWuckzZuVdDWLllDrQEQmpILnjyTb/QtSYHzZY1R0uQ0o6JeGd9Tc0VCiUhOJ497XTPiWzQh0qxar12jra+k9ZQwhaFTIKe0y2pRe51eaxyvrRurVb0kIhNI8a3t8lpT+sjii3Tj/r/SBK9DVuq6q972xuMp55R10l2Nx+jefx2tOXc/p6G7nx4AoKQEgUxHvim5y2SlwMm0d+abw9qu2bT9fRHIZGU6UoU9pgAA2FlXrck1t0iBLxONUWsAhKakgqfAGfk7rF2zMkqYiCZ4WZ1d84K2VFYqFUQVM74OjG9Ujc2p0ljFTURREytoAi51LdVzGa3JGb3aMkmbtlWpbHVM43/4bM8xvqTq06WHFx2qBVUvSeoOngr5Mvr/HjxWc657ttc+AAAkSc7JpLPKrVlbsDm3Zq282TN2XbUbm5WrbwhvfACA4a+PWuOyGWoNgNCUTPBkfKNlreP0dHKaPLNak718g/GIPFVZT+9MZCVt6zneKiYp1ue5cvK1JpfRyuxoLUvP0MttU/T6pnEa97uEqv73X332ZXrhuFF6wZ74lmOs6+z7uQAASJK2NipXX9/nLn/5ql0/31FlAAC7QK0BsJeVTvCUNVreMFaPRA7RuEirxiZaFTfb397Os5n6k5OvrX5az3bO0F+a5uq1beNV31ipyfdHVfHyeuX66cvkM9UUALC7duyz0d8f9PyhDwDYE9QaAEVid33I8GACqaM1rpXNY7Q6M1YdLqtgN+YXpVxOr2er9dfm2XppyyRt3lSjoD6u8udW9Fr6AADAUAlaW+WyNGkFAISHWgOgGEpmxpOc5LJWbam41mVGa7Nv5SmtChsf0Gyn7pBqm+/roaZD9ey6aepoSMq2eTK+If0HAAw9agsAYG+g3gAoopIJnoyTTMpT+7Yy/dE7SK82T9KoeIf2K9um91W9oLqI32cIFcipOUhpZS6m1dmxeq1zsp7aOEsd9UlNfdio/JFFkiQ/lSrCuwIAlLQtDfIbuvoP8qUAABCWTFb+itXUGgBFURrBk3Oa8Zs2BTFP7ZPi2nB6udraE4rFs1pWXqu4yenMqpc0VWlV2URP2NQcODUFMa3OTtTLnVO1qmOM1raN0rbGctU9KJW/sl45AicAgCRt3JL/bzwujRu9Z+dyTtpcr6CljS8BAIDtQqg1yubkfJ96A6BoSiN4kuT+8YqMpJq6qWrdb6qcJ3WOD7RpQkzPxGdqVmKTEvENipqMsi7QmlxUq7NjtSYzVq93jNfyllrVt5WrtSmp5GtxJf/6qnI0DAcAdOm+iYSJx+WVl0mSXCwqRQdZSoNApjOtXGOzFPhDPUwAwDC2c61xyYRkBnaTpALUGgD7kJIJnrrl1qzVxBvzTcCbPnKMtsZjei09SU9V7C9VS63RrUq5mP7eMUuLWqdoZfNYbd5WpVxrVCZtVb7O06TvPCs+ngEAfXHptHKr35QkRSZPkqupHMSTnUw62/N8AAD60l1rvDkzpYg3uPCJWgNgH1NywVMBJ5mckdo8/XHR2/RQ9GAZL5BzRi5npZyRfCOTMzKB0X6P+Uo8sWg37oUHAMAAbGlQrrunEwAAu+AvW6nIpImDu8hBrQGwjynp4GnMc5tUvbJakuQiRs6YnqsFJvClQF0/56Om6OotyqXTRRkrAGD4CRq2ybS3D/z49k56bAAABs45ag2AYa+kgyd/+SqZ5fmfBzI5NRfqaAAApSZIpSRuQgEACBG1BsBwZ4s9AAAAAAAAAJQmgicAAAAAAACEguAJAAAAAAAAoSB4AgAAAAAAQCgIngAAAAAAABAKgicAAAAAAACEguAJAAAAAAAAoSB4AgAAAAAAQCgIngAAAAAAABAKgicAAAAAAACEIlLsAQDYc+3nHK0tR1g5ScZJU/6UUfSJfxV7WACAErHlU/OVPqml53HNAxWq/OXfijgiAECpodaULoInoARs29/TsSe8LGuccs7qb+lDNCX7djlr5IyRJBnnlE+mVLDNBE6x1fXKrVlbxHcAANiXNc3LaNX8e3oez+i4VJHUUQN+fsXSbfJfXx7G0AAAJYJaU7oInoDhznqSKdx03Okvquy9WY2NtmlUpF3pIKqOIKas8xQ3OSW9tDw5Nftl2pKp1FO/OFLjf0jwBADozUQiknUF21aecqd0ysDPMfsXn9SML/JlAADQN2pNaSN4Aoa5dQ/sr7dNWKq4l1PM5iRJFV5aoyPtmhXfpP1jW7XNT2iLX6mOIK7RXpvGeW2qtFmt9yv0XPtsBXwSAAD6Yoze81K97qn6oaTyYo8GAFCKqDUlj6+bwDA3paZZB1du0NhIq2bEtkiStvkVavArtDw9QZtyNeoIYurw48o6T0kvrdFeu2ojLTootkXVXoecV+Q3AQDYZx2SWKuxHl8EAADhodaUNoInYJjzbKCkzWh0pE2TIy2yckq5qDblqvVmerQeWXGActmInJPkpHfOWqGJiRZ1xOJ6R2KrEjYrx/0tAQC76T83H6ambFnP429N/JNGeckijggAUGp2rjWS9L7RL+g9yXSRRoTBIHgChrm2TFxvpkdrW65cG7KjJEmNuXI1ZpPalinXzGsalVu7ruf4xb+fo7LxWVV7nbKSosYv0sgBAMNB1kWUdql+97/yoZny31jR8/jxNybqA+WN8gxXNQAAAzPYWiNJn7ztIq0666dhDw1DgOAJGOYqzm/WG3aUZI2kidt3BE5ygfyG9QXHj7+4Xn/57GH6yNlP7t2BAgCGH+f0o3edoB95/a/J9jeuLnj88+PeoS9+a4pWnvqzkAcHACgJu1FrMLwQPAHDnN+wbdDH2/T22+D5zrzF0QCAkS63afOgj3epupBGAwAoRYOtNRhemAMNAAAAAACAUBA8AQAAAAAAIBQET8AIZJyUdZ6aA6eUi8m4Yo8IAAAAAFCKCJ6AEWb9f86XOaJZkrTVL1NHEJOCIg8KAFAylv34aH1s/l+KPQwAQAmj1gwvNBcHRpjqEzdp/rhVStisGoJydfhxZjwBAIbM90+9V2eXtxV7GACAEkatGV6Y8QSMMDHP19hom0ZF2rUpW6M2Py7jF3tUAAAAAIBSxIwnYIQZk2jXEcmVmuS16qX0ZFmmOwEAAAAAQsKMJ2CEidhACZNVzATamqtSmx+XyJ4AAAAAACEgeAJGCJtMSkcdospIWl5X0rQtV67HV89VcivdxQEAe8ZWVip76hEa7RX23Phm/VwlN5kijQoAUEqoNcMTS+2AkWLWfpryw1V6W8U6+TJKOU+NuaSm/Ue7cisXF3t0AIBhzn/bTD159x29tj9z3mGasPjZIowIAFBqqDXDE8ETMIJEbL6LeGtQJl9WgTOSY50dAAAAACAcBE/ASGGMoiZQs1+mZekJkqR0wEcAAAAAACA8fOsERhBrAjXnytScK1PWecoFXrGHBAAAAAAoYQRPwEjhnNpzcXWamAJnFMjIZ6kdAGAI+a6Pm1VQZwAAQ4haM/wQPAEjhFuyXJs+MK5wYxAot3l9cQYEACgp9vklOuvY9/faHry5sgijAQCUImrN8ETwBIwQLpdTbv2GYg8DAFCiXDaj3MrVxR4GAKCEUWuGJ1vsAQAAAAAAAKA0ETwBAAAAAAAgFARPAAAAAAAACAXBEwAAAAAAAEJB8AQAAAAAAIBQEDwBAAAAAAAgFARPAAAAAAAACAXBEwAAAAAAAEJB8AQ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qDYCRRPA0RDadVnr9hh7bt915rP5YMEeSZILOnXc+X/mJlZoarVNdkKfGoEGuYxQ3EbmO0axIkxpjWyVJqydPDPsSAACjWeDL317TY/P0RUml424vB2RsOi4iP6//Dwt+zCqdz61sABj3qDUARhDB0zApveO5fp/fdNZc1U7I1xa3WCVOq+ImLUeO8kxUU1xXjZFapWxE5fkNqj94P0mSs61W6c1bRqL5GCkd83zJsnQtgKFxF72qvj8KSO6RCwf8MCBJQUSKzJopSbJ19fLr6oephcg2E4vJnTpFkhTU1CpobMxyiwCMNdQaAGEgeBohfmC0rb1QvnXkGV+e2aopbkqecRUznqoiVo626SNlr+jh2w5UIKNX7j1QU68neMoZxshJZIZh2mRSNp3OcoMAjEepokBLvzxNkjTt6alK/PmFLLcIw8WZPVNLL870nK76yyS5i17NcosAjFfUGgC7IngaIdYaNadjcoxVm/XUZl1V+1YttlVT3TxJkmus4ial/EhSzelYlluM4eYWFip10Gylij15TWmZtFWyNKpksSM/amR3u73kJqWCDe3ynn5LNtWenUYDAMakdafG5J6wcFD7Vv3sbfkNDSG3CACQC9x9qrT601OHfBy1ZnwjeBoh3r2leuKEQp120GJtSpUo30mqzG2Q1KZ60ybPOHKVCZ5iTlqtJirL0OicYooKVXNAnppmSl6TJ1mpZWZaRVPrVZrforxIqtv+1c0FWv/mRO2zrkKqa5DSadlUOtNbyvcZrgegm4qn21R9UFxNs4KBd0bO8/PsoIbDyEo7PrC/3O4lSMVvbJe/bEU4jQMwZlFrxrljDtK2A/LVXjzEv/9eak3RX15X0NY2vO3DqEXwNEJK/vs5Bd6xerpkjl6KztQnql6Vl5dWvkmpPkip1LFyJMWdlBJuu5r9mNpLrNz5+/CLX46wibiaZkqlh1Srpd2T5/o6e/q7+mjJS5odadcEJ0+BrJyONWyXppL614KPaPviSiWqJyrSmFKkrkWmeoeCxibZZDLLVwRgNHEXvaqJ3hFKJ6KSpGSplY30Hzy05zsqqpim9MZNI9FEjEZGqj6i5+ZYXYm8ZSPfHACjG7VmfKudn6/aBXtw83v3WmOloiWz5KzdyHyE4wTB0wgqvf05ld6e+f6Rx/bTlOn1KnFaVKiUfFl5Ripx2lQVq1aLH9X7Tn9Nzx9UqbJzsttuDBNjFMSszp7+to5KrNSMSL3ynUClTkSeiSqQVaBAkiNJqowY3Vh1r578t330f1sP1ZK1U5VYUqbyFwsVe3cLxRtAD94jL2v6I5nv1313odon9P/L4fbDrBqqZmnmNbyfAAAGh1qDvWakdz9fosoHC+Q98nK2W4MRQPA0wtx9qrT9xogWTlittsBTykY0xc38NbgKJCetA2MbtDVVrJR1ZQzDqcY640Xlzpim5n0myBppbetE7RffpPqgTc+2VujxHZlVDCfFmnRK8WK5JlCJ06ISx6rMNTo9f4Uqptfq3viR+ufaA+Ukfdn21ACvCmC8ipRP0apL5yhVwDCIccEYbfjWsQpiUhCxkvi9AQAAjC4ETyPNi+jQsg2aGq1XuVevKW6TPOPJkaOUTarFGq1Pl6o+nad04Moy0dPY5xhZLyIFUmKLoyfenadlkyerIJrU+roSNa8rkiTZhK9XZsxQwktpv+ItOiB/o47LW6FpEUfzvRodVrRWi4oPkJ8XkRfpb6FbAOOa6w597gWMaanCQc7nBADDxC0qUsNp+/HeA2BQCJ72QqR8iuzEElnTEQ45HU8YI/m7vAnvsr15ZqFKvOWa4tVrWqRW5a4UUSZE8GXVGHha0z5JTX5Myxomq3ZTscpG7IoQisDKtKcUrWtXyQpHbmuetpfkaVtEijZI5RszHxBTiYhq15Vra5HVptlFaq6IaV50i2ZEWlTmRrRvbJOcSUklJ0QVj7PqITAeuCXFMoWFQzrGn1w86H0jLUbxHUNtFUabeI2RH+v/RlWq0MqP8wERQE97Wms2v8doML0sqTW5I9YYKF6d+eyanBjIOpKTMorWma7HQG8InvbCmgvn6JgPvqk8NyXHBMpzU0o47SqNNGt562QF1lHE8eWZzFeBm1RxpEUVXq1mRWpU6qbkmY5hdibzf6kvoxY/prR1teXBGZr302ezeYkYBjbVrvTqtTKr1yohKdHXjo6rSfkJtR85T5uOK9bL7gydOqFYirUoz0Q1K1KvwyrX6Z2q+SpYXSCtHsGLAJAV9aftpy3Hhnf+ya8ESvz5hfBeAOGzVlOvH/h3hbr/d6y2HzYC7QEw5lBrMFiJP7+Q+SxjjNZce4zSeVZ5W42m/uy5rsdiwA56QfA0AHfBfG39DyvHqNt8S1HX1+HFb2tOolpTvHp5Jq1oR8AUd9olSZ5JZ1apc5KKGl8Jk1SJ26IyJ6lCx8gzrjzT0dvJZqaVDqyjgP9bxx/HlZOfkJlerlXnGx23/9v64MTXdVzeZsVMXJIyveHqSxWrs3JaU/Kz3GQAwyMyY7pWfW5mr89lhjDQSwUAsHeoNRhW1mrODSskx0jtKfkdj7d9cJ89W/UOOY/gaQBBNKJDytYq4vhyjVXE+HKM1YRIS2a4nFermZFaxYyvQEYp66jBxjQ/tkn5TlKFTrvyTVqOJM9IUWPkyZFnHHnGldMxDi8tX23WqtlG1RZ48pnbadyIzJqpdHmJmsvjqp8V0bH7vqOzJ76hQ2KbVOzElLK+dgTtej05W9XrJmjW+rRMQ3O2mw1guERctZcwJxPC0/aBo9RcMbghMQByFLUGw8yvru72uPmYKrVNotagdwRPg+QaqzynXXluSnEnpcleg2Z6NaqM1Gq25ykiT0mbVpNNyfGtCt0mFTpGhU60q8eKbzNv9kmblmuMHGXCp5T11Ri0a4fvqc7PVzKIKGCAbG4zRiYalVNYoOYFU1S7j6fmmYHy59Tq3Mkvab63TQkjVftJbfc9LW2foYdqDlDhiojyVm9TUN+Q7SsAAIxSkYppktPxe4RjtHmhKz8+yA+cVorVOoo0p8NrIAAgJ7gTJsgU5A+91mDcIXgapIjxNdFr1hSvXjO8GpW4LYobX3ETyJEj1ziKKSLPuCp2Ajny5OwyZC4zlM4qZf2Onk6Z51LWV1OQ1LJUnla2T9a69kna0Z6vQEaGsDg3GSOnoEBmerm2HjdJOw7zVTFri94/ZZXOKX5VB0ST2pA2erqtQk/W76d/bqxSw458RTd6mvliq4JV62RT7dm+CgBjFbUltxmj5V+q3G2lqUH+pXfsNuumd+TX1g570wCMI9SacWH7Ofuq5pDOv+wh/qXzb2RcIXgahEBGySCibalCtQRRbU8XKuak5MrKM2kVum06LLZe0yJGxU6efGuUlq/6oF31QWalOs8EKnR8JYzRDj+lLX6+GoO4Gv08tVlPW1PFqk/n6Zltc1T4xcz/hdPrlzKPTw4yriuntEQ7Dpmoky99XudNeEFlbrsSxihhPMVMTIvbJ+j3W47SG6/MUcU/Ak1d2yx32yYFNTsUEDoB2AvRBkezb10pSQoam8S9SXSKVzuadcdKpevqst0UAGMctQb9stL8W6tlN2zm38Y4QfA0AHdLjV6750BZI8mR7C5fnawjXfrpv+u4xLsqdZvkWyklox1+XHVBQnV+Qp7xNdFtUqHTpi1+kTamSlWbzlejH1fKurr/r8cqWmcUrbfKW/Vc1q4X4bOBlYJMuJhw21XmtmuSE1XMRBR0RP/lkXodWLxJa+aUalvDRE1ryVN8zSYFbW3ZbDqAENi6BlU8OWXA/bYc68qP7eHtQStNfknyWgJFmlNKb9m6Z+dBTnN88W8DyFHUGow61TsUtLRkuxUYIQRPA0hv3qIpv9gy4H63HvFePVc+W4VeJhhIB64a0zG1pKNqTXtyjFWh16a4m1ZDKq7atjy1pSJK+a6sNZpz+0al16wL+3IwGgS+bHu7Yg2+Ht88X/PiW7QguknTIq1KGFcx42lWpElnFL6pqnnVuq/4UK2qn6Pp6yfJSadl29tlU2kpoD8ckAv82lrl3f/igPvlVS1UqmDPF56Y8NjyHhOBIjfFdhglJ2nIHx4Dt2N+KElBXb2CZhayAHIFtQajTvkkRRJ5su0p/s2MA8ZaO6jfSk51Ph52W4Bxw4nH5ZRPVu3R07TlvVZT51brlKnL9NHiVzS7Iw5O2UDNNtDLyXJd+cLHVfRsnorWp5VY2yCzcZv8mh3ZvQjstUeDe7PdhFGFOgMMn9oLjlXNwR2/4u3BZ8jKB9PyHnl5eBuFEUed6YlaAwyfrlqzFwuy521xVPGjZ4evURhxg6k19HgCsiBoa5PdsEklO+oUq5+vHfPLdVflZP3f7IM0a0KtpifqND1Wq/nxzSpxm3Xy3Hf1QsFMrduer6LFpSp7PaHokoj86hp6PgEAeph0/xKVPZInOY6WX1YpPz603k8bT/DkHr1QkjTrl4vl19WH0UwAwBg26f4lKnl3llaem5ftpmCUI3gCssSm0/IbGpRYvFnRulK1rYqraWmJVk6coMWTA5nJSR00Y4POmfyGjilaqQUFG7Wtski/jxwpryGmydsmyNTVyyYJngAA3fl19VJHWDTt6QrVHOCpZergp3BNJ6zSCZtZdch1Q2olAGAs8+vqFVm+QRX/mNO1LVnkavuhLFmH7giegCxLr98gs3mLErGYCooKFUwsUUtVkeqr8vRaa6XmFGzXWcVvaP9oo3xr9cL0Wdo2YbqCPE/GGFYiBQD0K/bAS0pMPFYtU7PdEgBArvG31yjvvpqux4VVlWqcWSFJShfYPZ+sHjmF4AkYBWw6Lev7ssmktKNW+dXFyttQJjdZpGcqZmtu3lZNcZv0anKmVq0oV+U7KZm3V7DKHQAAAIBRI716rWZcu1aSVP/pY1R9eJYbhFGB4AkYLayVTaeldFq2pVVOS1ImkKYkmjQ50qBCJ9CC6CZNmblDO+ZP1rRtc+W8tZzwCQAwoIl/XaJJj+2cg2PrWVW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