Multi_Class_Segmentation.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install nibabel\n",
        "!pip install pytorch-lightning\n",
        "!pip install torchmetrics\n",
        "!pip install -U albumentations\n",
        "!pip install segmentation-models-pytorch\n",
        "!pip install -U opencv-python-headless==4.1.2.30"
      ],
      "metadata": {
        "id": "41TTZn8vLDg-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip list | grep opencv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgagDQN8Lqyv",
        "outputId": "fd4a6375-26c0-4166-c666-a78d55015731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "opencv-contrib-python         4.1.2.30\n",
            "opencv-python                 4.1.2.30\n",
            "opencv-python-headless        4.1.2.30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-taYHoUnHZcU",
        "outputId": "bb77a35d-d179-4e3c-840f-3508c8992a02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-26 15:56:48--  https://data.mendeley.com/api/datasets-v2/datasets/zm6bxzhmfz/zip/download?version=1\n",
            "Resolving data.mendeley.com (data.mendeley.com)... 162.159.133.86, 162.159.130.86\n",
            "Connecting to data.mendeley.com (data.mendeley.com)|162.159.133.86|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘data.zip’\n",
            "\n",
            "data.zip                [         <=>        ] 156.22M  35.0MB/s    in 5.1s    \n",
            "\n",
            "2022-03-26 15:56:54 (30.4 MB/s) - ‘data.zip’ saved [163813707]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "if os.path.isfile('data.zip') is False:\n",
        "  !wget https://data.mendeley.com/api/datasets-v2/datasets/zm6bxzhmfz/zip/download?version=1 -O data.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "from zipfile import ZipFile\n",
        "if os.path.isdir('data') is False:\n",
        "  with ZipFile('data.zip') as zf:\n",
        "    zf.extractall('data')"
      ],
      "metadata": {
        "id": "yW8PUSIAKaax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from glob import glob\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "7f8L8ALKK6H1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images=sorted(glob('./data/images/*.nii.gz'))\n",
        "labels=sorted(glob('./data/labels/*.nii.gz'))\n",
        "data_dicts=[{\n",
        "    \"image\":image_name,'label':label_name}\n",
        "    for image_name,label_name in zip(images,labels)\n",
        "    ]"
      ],
      "metadata": {
        "id": "LOyEaE1SMiOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_files,val_files=train_test_split(data_dicts,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "Ix2TAGYUMt3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_files[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4a-u7glNHvo",
        "outputId": "e34f4309-6ad2-48ce-d662-d44575d8b168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'image': './data/images/image_019.nii.gz',\n",
              " 'label': './data/labels/label_019.nii.gz'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nibabel as nib\n",
        "image=nib.load(train_files[0]['image']).get_fdata()\n",
        "mask=nib.load(train_files[0]['label']).get_fdata()\n",
        "print(image.shape,mask.shape)\n",
        "print(mask.max(),mask.min())\n",
        "fig,ax=plt.subplots(1,2)\n",
        "ax[0].imshow(np.squeeze(image,-1),cmap='gray')\n",
        "ax[1].imshow(np.squeeze(mask,-1),cmap='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Xw16qnB_NXxD",
        "outputId": "51d8ab49-77cd-433c-e6ac-144d37b2c2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(512, 324, 1) (512, 324, 1)\n",
            "2.0 0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9403216390>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "import cv2\n",
        "from torch.utils.data import DataLoader,Dataset\n",
        "import torch"
      ],
      "metadata": {
        "id": "g0oXqw3rNbB1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_aug=A.Compose(\n",
        "    [\n",
        "     A.Resize(512,512),\n",
        "     A.HorizontalFlip(p=0.5),\n",
        "     A.VerticalFlip(p=0.5),\n",
        "     A.Rotate(10),\n",
        "     A.GaussNoise(p=0.1),\n",
        "     A.Normalize(mean=(0),std=(1)),\n",
        "     ToTensorV2(p=1.0)\n",
        "    ]\n",
        ")\n",
        "\n",
        "\n",
        "val_aug=A.Compose(\n",
        "    [\n",
        "     A.Resize(512,512),\n",
        "     A.Normalize(mean=(0),std=(1)),\n",
        "     ToTensorV2(p=1.0)\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "o1YlslbINbEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DataReader(Dataset):\n",
        "  def __init__(self,data,transform):\n",
        "    self.data=data\n",
        "    self.transform=transform\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    image_path=self.data[index]['image']\n",
        "    label_path=self.data[index]['label']\n",
        "\n",
        "    image=nib.load(image_path).get_fdata()\n",
        "    label=nib.load(label_path).get_fdata()\n",
        "\n",
        "    transformed=self.transform(image=image,mask=label)\n",
        "    image=transformed['image']\n",
        "    label=transformed['mask']\n",
        "    label=torch.moveaxis(label,2,0)\n",
        "\n",
        "    return image,label"
      ],
      "metadata": {
        "id": "ntNp1KFwNbHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_data=DataReader(data=data_dicts,transform=train_aug)\n",
        "sample_loader=DataLoader(sample_data,batch_size=4)\n",
        "sample_batch=next(iter(sample_loader))"
      ],
      "metadata": {
        "id": "5XbueBZ5NbJ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_batch[0].shape,sample_batch[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GU4Z1gyRhyd",
        "outputId": "71658cc8-c968-4e2d-9392-11657b5881cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([4, 1, 512, 512]), torch.Size([4, 1, 512, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "grid_image=torchvision.utils.make_grid(sample_batch[0],4,1)\n",
        "plt.imshow(grid_image.permute(1,2,0))\n",
        "plt.title('batch of images')\n",
        "plt.figure()\n",
        "grid_image=torchvision.utils.make_grid(sample_batch[1],4,1)\n",
        "plt.imshow(grid_image.permute(1,2,0))\n",
        "plt.title('batch of masks')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "FoD_ZAsWRkFJ",
        "outputId": "68ae9156-02d0-44fb-8cad-e1001992fddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'batch of masks')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import segmentation_models_pytorch as smp\n",
        "from pytorch_lightning import seed_everything,LightningModule,Trainer\n",
        "import torchmetrics\n",
        "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n",
        "from segmentation_models_pytorch.losses import DiceLoss"
      ],
      "metadata": {
        "id": "v_gaIob0SJ0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class SegmentClass(LightningModule):\n",
        "  def __init__(self):\n",
        "    super(SegmentClass,self).__init__()\n",
        "\n",
        "    #model\n",
        "    self.model = smp.Unet(\n",
        "        encoder_name=\"resnet34\",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7\n",
        "        encoder_weights=\"imagenet\",     # use `imagenet` pre-trained weights for encoder initialization\n",
        "        in_channels=1,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)\n",
        "        classes=3,                      # model output channels (number of classes in your dataset)\n",
        "    )\n",
        "    #loss function\n",
        "    self.loss=DiceLoss(mode='multiclass',classes=3,from_logits=True)\n",
        "    #metric\n",
        "    self.iou=torchmetrics.IoU(3)\n",
        "    #batchsize\n",
        "    self.batchsize=24\n",
        "    #lr\n",
        "    self.lr=1e-3\n",
        "\n",
        "  def forward(self,x) :\n",
        "    return self.model(x)\n",
        "      \n",
        "  def configure_optimizers(self):\n",
        "    opt=torch.optim.AdamW(self.parameters(),lr=self.lr,weight_decay=1e-5)\n",
        "    scheduler=CosineAnnealingWarmRestarts(opt,T_0=10,T_mult=1,eta_min=1e-5,last_epoch=-1)\n",
        "    return {'optimizer':opt,'lr_scheduler':scheduler}\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    data=DataReader(data=train_files,transform=train_aug)\n",
        "    loader=DataLoader(data,batch_size=self.batchsize,shuffle=True)\n",
        "    return loader\n",
        "\n",
        "  def training_step(self,batch,batch_idx):\n",
        "    image,segment=batch[0],torch.squeeze(batch[1],1).long()\n",
        "    out=self(image)\n",
        "    loss=self.loss(out,segment)\n",
        "    iou=self.iou(out,segment)\n",
        "    self.log('train_loss',loss,on_step=False,on_epoch=True,prog_bar=True)\n",
        "    self.log('train_iou',iou,on_step=False,on_epoch=True,prog_bar=True)\n",
        "    return loss\n",
        "\n",
        "  def val_dataloader(self):\n",
        "    data=DataReader(data=val_files,transform=val_aug)\n",
        "    loader=DataLoader(data,batch_size=self.batchsize,shuffle=False)\n",
        "    return loader\n",
        "\n",
        "  def validation_step(self,batch,batch_idx):\n",
        "    image,segment=batch[0],torch.squeeze(batch[1],1).long()\n",
        "    out=self(image)\n",
        "    loss=self.loss(out,segment)\n",
        "    iou=self.iou(out,segment)\n",
        "    self.log('val_loss',loss,on_step=False,on_epoch=True,prog_bar=True)\n",
        "    self.log('val_iou',iou,on_step=False,on_epoch=True,prog_bar=True)\n",
        "    return loss"
      ],
      "metadata": {
        "id": "Xah6Ji3ktYJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=SegmentClass()"
      ],
      "metadata": {
        "id": "yWTuCecW5luX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pytorch_lightning.callbacks import LearningRateMonitor\n",
        "lr_monitor=LearningRateMonitor(logging_interval='epoch')\n",
        "\n",
        "trainer=Trainer(max_epochs=50,gpus=1,precision=16,callbacks=[lr_monitor])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUK_19InujCb",
        "outputId": "a243957f-b6a2-40ac-b09d-be2d1a1689cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using 16bit native Automatic Mixed Precision (AMP)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.fit(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344,
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        },
        "id": "Gfsw8LAWujEz",
        "outputId": "2ad15cd4-b115-4679-c51a-a12764cf897c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name  | Type     | Params\n",
            "-----------------------------------\n",
            "0 | model | Unet     | 24.4 M\n",
            "1 | loss  | DiceLoss | 0     \n",
            "2 | iou   | IoU      | 0     \n",
            "-----------------------------------\n",
            "24.4 M    Trainable params\n",
            "0         Non-trainable params\n",
            "24.4 M    Total params\n",
            "48.861    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d3e2e93385f42359e88c0a612ddb848"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/data_loading.py:433: UserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
            "  f\"The number of training samples ({self.num_training_batches}) is smaller than the logging interval\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e11dc83716f9484d84b2cc9aaa8022cb"
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        {
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          "data": {
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a850046c1f3b4473999187c937b972ee"
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        {
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          "data": {
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ],
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        {
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          "data": {
            "text/plain": [
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    {
      "cell_type": "code",
      "source": [
        "model=model.to('cuda')\n",
        "test_loader=model.val_dataloader()\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  imgs,preds,labels=[],[],[]\n",
        "  for img,seg in test_loader:\n",
        "    pred=model(img.to('cuda'))\n",
        "    pred=torch.sigmoid(pred)\n",
        "    imgs.append(img)\n",
        "    preds.append(pred)\n",
        "    labels.append(seg)"
      ],
      "metadata": {
        "id": "6kbvTvxFujHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_array=torch.cat(imgs)\n",
        "preds_array=torch.cat(preds)\n",
        "labels_array=torch.cat(labels)\n",
        "imgs_array.shape,preds_array.shape,labels_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcFVRLzqujJ_",
        "outputId": "7f665edb-a87e-44d7-934e-ceb93a001b5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([28, 1, 512, 512]),\n",
              " torch.Size([28, 3, 512, 512]),\n",
              " torch.Size([28, 1, 512, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_array=torch.squeeze(imgs_array,dim=1)\n",
        "labels_array=torch.squeeze(labels_array,dim=1)\n",
        "preds_array=torch.argmax(preds_array,dim=1)\n",
        "imgs_array.shape,preds_array.shape,labels_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3RBBzb6ujM4",
        "outputId": "9d79eabe-d3a8-41db-d497-2949cae4c89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([28, 512, 512]),\n",
              " torch.Size([28, 512, 512]),\n",
              " torch.Size([28, 512, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_array=imgs_array.detach().numpy()\n",
        "labels_array=labels_array.detach().numpy()\n",
        "preds_array=preds_array.detach().cpu().numpy()"
      ],
      "metadata": {
        "id": "D--tysMTKjSo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax=plt.subplots(3,3,figsize=(16,8))\n",
        "for i in range(3):\n",
        "  ax[i,0].imshow(imgs_array[i,:,:],cmap='gray')\n",
        "  ax[i,0].set_title('image_{}'.format(i))\n",
        "  ax[i,1].imshow(labels_array[i,:,:],cmap='gray')\n",
        "  ax[i,1].set_title('segment_{}'.format(i))\n",
        "  ax[i,2].imshow(preds_array[i,:,:],cmap='gray')\n",
        "  ax[i,2].set_title('predicted_{}'.format(i))\n",
        "plt.subplots_adjust(wspace=-0.7, hspace=0.4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "Vr50ukN8KtUI",
        "outputId": "02d807c0-370b-4c7e-c089-491cbe8fa393"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "256XaAJuLCK5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}