Image cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchvision.models as models\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from DataLoader import ImageDataset\n",
    "from torch.utils.data import DataLoader\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31518\n",
      "9435\n"
     ]
    }
   ],
   "source": [
    "csv_path = \"./DL_info.csv\"\n",
    "Image_slices_dir = \"/home/parv/Dropbox/Final_Images_2/\"\n",
    "\n",
    "df = pd.read_csv(csv_path)          # The DL_info.csv file path\n",
    "df.sort_values(\"File_name\", inplace=True) \n",
    "df.drop_duplicates(subset =\"File_name\", \n",
    "                     keep = False, inplace = True) \n",
    "print (len(df.index))\n",
    "\n",
    "new_df = df[df['Train_Val_Test']!=1]\n",
    "print (len(new_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Dropout(p=0.5)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace)\n",
      "    (5): Dropout(p=0.5)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg16 = models.vgg16(pretrained=True)\n",
    "print (vgg16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Dropout(p=0.5)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg16.classifier = vgg16.classifier[:4]\n",
    "#print (vgg16)\n",
    "vgg16.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1887\n",
      "torch.Size([3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "train_dataset = ImageDataset(root_dir = Image_slices_dir, dataset_type = 1, \n",
    "                             output_image_size = 224)\n",
    "# Set the new_df yourself\n",
    "train_dataset.df = new_df\n",
    "\n",
    "#print('Dataset length: ' + str(len(new_dataset)))\n",
    "\n",
    "dataloader = DataLoader(dataset = train_dataset, batch_size = 5)\n",
    "print (len(dataloader))\n",
    "\n",
    "print(train_dataset[0]['image'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "curr_features = []\n",
    "filenames = []\n",
    "batch_no = 0\n",
    "for batch in dataloader:\n",
    "    vgg16.eval()\n",
    "    out = list(vgg16(batch['image'].cuda()).cpu().detach().numpy())\n",
    "    # print (len(out))\n",
    "    curr_features.extend(out)\n",
    "    # print (batch['Filename'])\n",
    "    filenames.extend(batch['Filename'])\n",
    "    batch_no+=1\n",
    "    print (batch_no)\n",
    "#     if batch_no==10:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_function = KMeans(n_clusters= 8)\n",
    "assigned_clusters = cluster_function.fit_predict(curr_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running model and saving \n",
    "# pickle.dump(cluster_function,open('./clustering_model/cluster_function.p','wb'))\n",
    "# pickle.dump(assigned_clusters, open('./clustering_model/assigned_clusters.p','wb'))\n",
    "# pickle.dump(filenames, open('./clustering_model/filenames.p','wb'))\n",
    "# pickle.dump(curr_features, open('./clustering_model/curr_features.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 7, ..., 5, 7, 7], dtype=int32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(assigned_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9435"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "assigned_clusters_2 = pickle.load(open('./clustering_model/assigned_clusters.p','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 7, ..., 5, 7, 7], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assigned_clusters_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original length is 9435\n",
      "New length is 246\n",
      "50\n",
      "Final length is 246\n",
      "Original length is 9435\n",
      "New length is 2065\n",
      "413\n",
      "Final length is 2065\n",
      "Original length is 9435\n",
      "New length is 1604\n",
      "321\n",
      "Final length is 1604\n",
      "Original length is 9435\n",
      "New length is 1229\n",
      "246\n",
      "Final length is 1229\n",
      "Original length is 9435\n",
      "New length is 2345\n",
      "469\n",
      "Final length is 2345\n",
      "Original length is 9435\n",
      "New length is 479\n",
      "96\n",
      "Final length is 479\n",
      "Original length is 9435\n",
      "New length is 650\n",
      "130\n",
      "Final length is 650\n",
      "Original length is 9435\n",
      "New length is 817\n",
      "164\n",
      "Final length is 817\n"
     ]
    }
   ],
   "source": [
    "list1 = [[], [], [], [], [], [], [], []]\n",
    "for i in range(1,9):\n",
    "    batch_no = 0\n",
    "    train_dataset = ImageDataset(root_dir = Image_slices_dir, dataset_type = 1, \n",
    "                             output_image_size = 224)\n",
    "    # Set the new_df yourself\n",
    "    print (\"Original length is\", len(new_df.index))\n",
    "    loop_df = new_df[new_df['Coarse_lesion_type']==i]\n",
    "    #loop_df = loop_df[loop_df['Train_Val_Test']==2]\n",
    "    train_dataset.df = loop_df\n",
    "    print (\"New length is\", len(loop_df.index))\n",
    "    dataloader = DataLoader(dataset = train_dataset, batch_size = 5)\n",
    "    print (len(dataloader))\n",
    "    for batch in dataloader:\n",
    "        vgg16.eval()\n",
    "        out = list(vgg16(batch['image'].cuda()).cpu().detach().numpy())\n",
    "        # print (len(out))\n",
    "        # print(cluster_function.predict(out))\n",
    "        list1[i-1].extend(cluster_function.predict(out))\n",
    "        #curr_features.extend(out)\n",
    "        # print (batch['Filename'])\n",
    "        #filenames.extend(batch['Filename'])\n",
    "        batch_no+=1\n",
    "        # print (batch_no)\n",
    "    print (\"Final length is\", len(list1[i-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "Counter({5: 24, 7: 21, 4: 16, 1: 15, 6: 10, 0: 9, 3: 9, 2: 4})\n",
      "Counter({5: 530, 0: 226, 7: 220, 4: 106, 3: 26, 6: 1})\n",
      "Counter({1: 206, 4: 199, 6: 109, 3: 96, 0: 76, 5: 58, 7: 45, 2: 37})\n",
      "Counter({5: 308, 0: 277, 7: 61, 4: 21, 3: 6, 2: 2, 1: 1})\n",
      "Counter({6: 419, 1: 260, 2: 161, 3: 66, 4: 59, 0: 52, 5: 30, 7: 23})\n",
      "Counter({5: 104, 7: 68, 0: 35, 4: 15, 3: 4})\n",
      "Counter({7: 118, 4: 66, 5: 58, 3: 28, 1: 24, 0: 20, 2: 8, 6: 3})\n",
      "Counter({7: 149, 5: 146, 4: 60, 0: 19, 3: 19, 2: 2, 6: 1})\n"
     ]
    }
   ],
   "source": [
    "print (type(list1[0]))\n",
    "\n",
    "for i in range(0,8):\n",
    "    print (Counter(list1[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "Counter({6: 32, 1: 27, 7: 21, 4: 18, 5: 17, 0: 13, 3: 6, 2: 4})\n",
      "Counter({5: 417, 7: 235, 0: 207, 4: 65, 3: 29, 1: 2, 6: 1})\n",
      "Counter({4: 193, 1: 168, 3: 142, 6: 83, 5: 68, 0: 52, 7: 38, 2: 34})\n",
      "Counter({5: 261, 0: 185, 7: 54, 4: 30, 3: 18, 1: 3, 2: 1, 6: 1})\n",
      "Counter({6: 457, 1: 288, 2: 256, 4: 84, 3: 80, 0: 47, 5: 35, 7: 28})\n",
      "Counter({5: 122, 7: 68, 0: 33, 4: 23, 3: 7})\n",
      "Counter({7: 107, 5: 69, 4: 59, 3: 39, 1: 19, 0: 18, 6: 11, 2: 3})\n",
      "Counter({7: 175, 5: 150, 4: 53, 0: 22, 3: 21})\n"
     ]
    }
   ],
   "source": [
    "print (type(list1[0]))\n",
    "\n",
    "for i in range(0,8):\n",
    "    print (Counter(list1[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "Counter({1: 42, 6: 42, 7: 42, 5: 41, 4: 34, 0: 22, 3: 15, 2: 8})\n",
      "Counter({5: 947, 7: 455, 0: 433, 4: 171, 3: 55, 1: 2, 6: 2})\n",
      "Counter({4: 392, 1: 374, 3: 238, 6: 192, 0: 128, 5: 126, 7: 83, 2: 71})\n",
      "Counter({5: 569, 0: 462, 7: 115, 4: 51, 3: 24, 1: 4, 2: 3, 6: 1})\n",
      "Counter({6: 876, 1: 548, 2: 417, 3: 146, 4: 143, 0: 99, 5: 65, 7: 51})\n",
      "Counter({5: 226, 7: 136, 0: 68, 4: 38, 3: 11})\n",
      "Counter({7: 225, 5: 127, 4: 125, 3: 67, 1: 43, 0: 38, 6: 14, 2: 11})\n",
      "Counter({7: 324, 5: 296, 4: 113, 0: 41, 3: 40, 2: 2, 6: 1})\n"
     ]
    }
   ],
   "source": [
    "print (type(list1[0]))\n",
    "\n",
    "for i in range(0,8):\n",
    "    print (Counter(list1[i]))"
   ]
  }
 ],
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