vis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# import os, sys, time, math\n",
    "# sys.path.append('/home/blanca/projects/deeplabv3/')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeppLabv3 -- num. of learnable parameters: 60996202\n",
      "torch.cuda.FloatTensor torch.cuda.FloatTensor\n",
      "out tensor([[[[1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          ...,\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.]]],\n",
      "\n",
      "\n",
      "        [[[1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          ...,\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "          [1., 1., 1.,  ..., 1., 1., 1.]]]], device='cuda:0',\n",
      "       grad_fn=<SoftmaxBackward>)\n",
      "target tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          ...,\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
      "\n",
      "\n",
      "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          ...,\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "          [0., 0., 0.,  ..., 0., 0., 0.]]]], device='cuda:0')\n",
      "torch.Size([2, 1, 300, 300]) torch.Size([2, 1, 300, 300])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/blanca/projects/deeplabv3/engine.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x = torch.nn.functional.softmax(x)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "1only batches of spatial targets supported (non-empty 3D tensors) but got targets of size: : [2, 1, 300, 300]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-7f4057c1ed6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;31m# train for one epoch, printing every 10 iterations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_loader_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m         \u001b[0;31m# update the learning rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/blanca/projects/deeplabv3/engine.py\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, optimizer, data_loader, device, epoch, print_freq)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_aux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loss\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/blanca/projects/deeplabv3/engine.py\u001b[0m in \u001b[0;36mcriterion\u001b[0;34m(inputs, target)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mlosses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction)\u001b[0m\n\u001b[1;32m   2054\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2055\u001b[0m         \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2056\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnll_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2057\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mnll_loss\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction)\u001b[0m\n\u001b[1;32m   1871\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnll_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1872\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1873\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnll_loss2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1874\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1875\u001b[0m         \u001b[0;31m# dim == 3 or dim > 4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: 1only batches of spatial targets supported (non-empty 3D tensors) but got targets of size: : [2, 1, 300, 300]"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "from PIL import Image\n",
    "\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from pathlib import Path\n",
    "from torchvision.models.segmentation import deeplabv3_resnet101\n",
    "\n",
    "import engine\n",
    "import transforms as T\n",
    "import utils\n",
    "\n",
    "from dataset import SegDataset\n",
    "from open_images import OpenImagesDataset\n",
    "\n",
    "from models.deeplabv3 import DeepLabHead\n",
    "from models.fcn import FCNHead\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='2 3'\n",
    "\n",
    "\n",
    "def load_model(pretrained=False):\n",
    "\n",
    "    model = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=pretrained)        \n",
    "    np_model = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    print(f'DeppLabv3 -- num. of learnable parameters: {np_model}') \n",
    "\n",
    "    return model\n",
    "\n",
    "def get_transform(train):\n",
    "\n",
    "    transforms = []\n",
    "    transforms.append(T.ToTensor())\n",
    "#     if train:\n",
    "#         transforms.append(T.RandomHorizontalFlip(0.5))\n",
    "    return transforms\n",
    "\n",
    "if __name__== \"__main__\":\n",
    "    \n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    dbroot = '/datasets/OpenImages/processedv4'\n",
    "    dataset_train = SegDataset(os.path.join(dbroot, 'test'), get_transform(train=True))\n",
    "    dataset_val = SegDataset(os.path.join(dbroot, 'validation'), get_transform(train=False))\n",
    "\n",
    "    # define training and validation data loaders\n",
    "    data_loader_train = torch.utils.data.DataLoader(\n",
    "        dataset_train, batch_size=2, shuffle=True, num_workers=4)\n",
    "#         collate_fn=utils.collate_fn)\n",
    "\n",
    "    data_loader_val = torch.utils.data.DataLoader(\n",
    "        dataset_val, batch_size=1, shuffle=False, num_workers=4)\n",
    "#         collate_fn=utils.collate_fn)\n",
    "\n",
    "    # model\n",
    "    model = load_model(pretrained=True)\n",
    "    num_classes = 1\n",
    "    aux=True\n",
    "    if aux:\n",
    "        inplanes = 1024\n",
    "        aux_classifier = FCNHead(inplanes, num_classes)\n",
    "\n",
    "    inplanes = 2048\n",
    "    classifier = DeepLabHead(inplanes, num_classes)\n",
    "    \n",
    "    model.classifier = classifier\n",
    "    model.aux_classifier = aux_classifier\n",
    "\n",
    "    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "    model.to(device)\n",
    "\n",
    "    # construct an optimizer\n",
    "    params = [p for p in model.parameters() if p.requires_grad]\n",
    "    optimizer = torch.optim.SGD(params, lr=0.005,\n",
    "                                momentum=0.9, weight_decay=0.0005)\n",
    "    # and a learning rate scheduler\n",
    "    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
    "                                                   step_size=9,\n",
    "                                                   gamma=0.1)\n",
    "\n",
    "    # let's train it for 10 epochs\n",
    "    num_epochs = 30\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        # train for one epoch, printing every 10 iterations\n",
    "        engine.train_one_epoch(model, optimizer, data_loader_train, device, epoch, print_freq=10)\n",
    "        # update the learning rate\n",
    "        lr_scheduler.step()\n",
    "        # evaluate on the test dataset\n",
    "        # evaluate(model, data_loader_test, device=device)\n",
    "\n",
    "\n",
    "    # for i in range(len(dataset)):|\n",
    "    #     it = dataset.__getitem__(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(dataset_train)):\n",
    "    it = dataset_train.__getitem__(i)\n",
    "    ims, labs = it\n",
    "    print(ims.shape, labs['mask'].shape)\n",
    "    if i == 10: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ims = ims.numpy().transpose((1,2,0))\n",
    "\n",
    "boxes = labs['boxes'].numpy()\n",
    "polys = labs['polys']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f82a225ddd8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ims1 = np.ascontiguousarray(ims)\n",
    "cv2.polylines(ims1, [polys], False, (255, 255, 255), 1)\n",
    "plt.figure(figsize=(50, 50))\n",
    "plt.imshow(ims1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f82a1968198>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = labs['mask']\n",
    "plt.figure(figsize=(50, 50))\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]], dtype=torch.float64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(mask, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "model = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=True, aux_loss=False)\n",
    "\n",
    "# new_model.backbone = model.backbone\n",
    "# new_model.classifier = model.classifier\n",
    "# new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "modules=list(model.modules())[:-1]\n",
    "m2=torch.nn.Sequential(*modules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModuleList(\n",
       "  (0): IntermediateLayerGetter(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu): ReLU(inplace)\n",
       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (layer1): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "    )\n",
       "    (layer2): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "    )\n",
       "    (layer3): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (4): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (5): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (6): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (7): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (8): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (9): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (10): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (11): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (12): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (13): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (14): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (15): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (16): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (17): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (18): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (19): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (20): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (21): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (22): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "    )\n",
       "    (layer4): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): DeepLabHead(\n",
       "    (0): ASPP(\n",
       "      (convs): ModuleList(\n",
       "        (0): Sequential(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU()\n",
       "        )\n",
       "        (1): ASPPConv(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU()\n",
       "        )\n",
       "        (2): ASPPConv(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU()\n",
       "        )\n",
       "        (3): ASPPConv(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU()\n",
       "        )\n",
       "        (4): ASPPPooling(\n",
       "          (0): AdaptiveAvgPool2d(output_size=1)\n",
       "          (1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (3): ReLU()\n",
       "        )\n",
       "      )\n",
       "      (project): Sequential(\n",
       "        (0): Conv2d(1280, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU()\n",
       "        (3): Dropout(p=0.5)\n",
       "      )\n",
       "    )\n",
       "    (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): ReLU()\n",
       "    (4): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.nn.ModuleList([model.backbone, model.classifier])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FCNHead(\n",
       "  (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU()\n",
       "  (3): Dropout(p=0.1)\n",
       "  (4): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.backbone\n",
    "model.classifier\n",
    "model.aux_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-21-c12811a4c4a2>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-21-c12811a4c4a2>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    model.backbone.\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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