train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "import json\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter \n",
    "from torchvision import transforms, utils\n",
    "from torchsummary import summary\n",
    "\n",
    "from Data_Loader import Pose_Dataset, my_collate, RandomFlip, RandomRotate, ToTensor\n",
    "from model import VRUNet\n",
    "from Modify_annotations import scale_annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parameters here before training\n",
    "\n",
    "# fraction of validation dataset, default = 5%\n",
    "# val_fraction = 0.05 \n",
    "\n",
    "# train, val batch size\n",
    "train_batch_size = 8\n",
    "val_batch_size = 16\n",
    "\n",
    "# use multiple GPUs if available, default = True\n",
    "multi_GPU = True\n",
    "\n",
    "# number of epochs\n",
    "num_epochs = 20\n",
    "\n",
    "# annotations and image path\n",
    "annotations_path = 'Data/COCO/annotations/person_keypoints_train2017.json'\n",
    "img_path = 'Data/COCO/train2017/'\n",
    "\n",
    "val_annotations_path = 'Data/COCO/annotations/person_keypoints_val2017.json'\n",
    "val_img_path = 'Data/COCO/val2017/'\n",
    "\n",
    "# don't modify this\n",
    "# scaled_annotations_path = annotations_path[:-5] + '_copy.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale_annotations(annotations_path, scaled_annotations_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of images in training set : 262465\n",
      "No. of images in validation set : 11004\n"
     ]
    }
   ],
   "source": [
    "train_dataset = Pose_Dataset(annotations_path = annotations_path,\n",
    "                      img_path = img_path,\n",
    "                      transform = transforms.Compose([\n",
    "                          RandomFlip(0.5),\n",
    "                          RandomRotate(degree = 10),\n",
    "                          ToTensor()\n",
    "                      ]),\n",
    "                       dataset = 'COCO'\n",
    "                      )\n",
    "val_dataset = Pose_Dataset(annotations_path = val_annotations_path,\n",
    "                           img_path = val_img_path,\n",
    "                           transform = transforms.Compose([\n",
    "                               RandomFlip(0.5),\n",
    "                               RandomRotate(degree = 10),\n",
    "                               ToTensor()\n",
    "                           ]),\n",
    "                           dataset = 'COCO'\n",
    "                          )\n",
    "\n",
    "print(\"No. of images in training set :\",len(train_dataset))\n",
    "print(\"No. of images in validation set :\",len(val_dataset))\n",
    "\n",
    "# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [len_train_images, len_val_images]) \n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size = train_batch_size, shuffle = False, collate_fn = my_collate)\n",
    "val_dataloader = DataLoader(val_dataset, batch_size = val_batch_size, shuffle = False, collate_fn = my_collate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize batch of images from dataloader\n",
    "iterator = iter(train_dataloader)\n",
    "images, target = next(iterator)\n",
    "# images, target = next(iterator)\n",
    "fig = plt.figure(figsize = (20,20))\n",
    "axes = []\n",
    "for i in range(images.shape[0]):\n",
    "    img = np.transpose(images[i].cpu(), (1,2,0))\n",
    "    axes.append(fig.add_subplot(4, 4, i+1))\n",
    "    plt.imshow(img[:, :, [2, 1, 0]])\n",
    "    plt.imshow(target[i][16].cpu(), alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VRUNet(\n",
       "  (Backbone): BackBone(\n",
       "    (bb1): Sequential(\n",
       "      (0): Conv(\n",
       "        (conv): Conv2d(3, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "        (batchnorm): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "        (batchnorm): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (2): Conv(\n",
       "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (3): Conv(\n",
       "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (bb2): Sequential(\n",
       "      (0): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (2): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (3): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (bb3): Sequential(\n",
       "      (0): Conv(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "        (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "        (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (2): Conv(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (3): Conv(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (skip1): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (skip2): Sequential(\n",
       "      (0): Conv(\n",
       "        (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (upsample1): Sequential(\n",
       "      (0): DeConv(\n",
       "        (convTranspose): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (2): DeConv(\n",
       "        (convTranspose): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (3): Conv(\n",
       "        (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (4): Conv(\n",
       "        (conv): Conv2d(32, 17, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(17, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (Unet_module): ModuleList(\n",
       "    (0): Unet(\n",
       "      (conv1): Conv(\n",
       "        (conv): Conv2d(20, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (conv2): ModuleList(\n",
       "        (0): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv3): ModuleList(\n",
       "        (0): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (1): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (2): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (3): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "      )\n",
       "      (conv4): ModuleList(\n",
       "        (0): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv5): Conv(\n",
       "        (conv): Conv2d(32, 17, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(17, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (1): Unet(\n",
       "      (conv1): Conv(\n",
       "        (conv): Conv2d(20, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (conv2): ModuleList(\n",
       "        (0): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv3): ModuleList(\n",
       "        (0): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (1): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (2): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (3): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "      )\n",
       "      (conv4): ModuleList(\n",
       "        (0): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv5): Conv(\n",
       "        (conv): Conv2d(32, 17, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(17, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (2): Unet(\n",
       "      (conv1): Conv(\n",
       "        (conv): Conv2d(20, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "      (conv2): ModuleList(\n",
       "        (0): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Downsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (2): Conv(\n",
       "              (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
       "              (batchnorm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv3): ModuleList(\n",
       "        (0): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (1): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (2): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "        (3): DeConv(\n",
       "          (convTranspose): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2), padding=(1, 1))\n",
       "          (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU()\n",
       "        )\n",
       "      )\n",
       "      (conv4): ModuleList(\n",
       "        (0): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Upsample(\n",
       "          (conv): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "              (relu): ReLU()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv5): Conv(\n",
       "        (conv): Conv2d(32, 17, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batchnorm): BatchNorm2d(17, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = VRUNet()\n",
    "if torch.cuda.device_count() > 1 and multi_GPU:\n",
    "    print(\"Using \",torch.cuda.device_count(), \" GPUs for training!!\")\n",
    "    model = nn.DataParallel(model)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up tensorboard\n",
    "writer = SummaryWriter('runs/VRU_Net_experiment1')\n",
    "dataiter = iter(train_dataloader)\n",
    "images, targets = dataiter.next()\n",
    "img_grid = torchvision.utils.make_grid(images)\n",
    "writer.add_image('train_loader image batch', img_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kartikaeya\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:2506: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.80 GiB already allocated; 1.27 MiB free; 2.93 GiB reserved in total by PyTorch)\n",
      "Error occurs, No graph saved\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.80 GiB already allocated; 1.27 MiB free; 2.93 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-5a2374704e86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\tensorboard\\writer.py\u001b[0m in \u001b[0;36madd_graph\u001b[1;34m(self, model, input_to_model, verbose)\u001b[0m\n\u001b[0;32m    705\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'forward'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    706\u001b[0m             \u001b[1;31m# A valid PyTorch model should have a 'forward' method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 707\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_file_writer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_to_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    708\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    709\u001b[0m             \u001b[1;31m# Caffe2 models do not have the 'forward' method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\tensorboard\\_pytorch_graph.py\u001b[0m in \u001b[0;36mgraph\u001b[1;34m(model, args, verbose)\u001b[0m\n\u001b[0;32m    289\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Error occurs, No graph saved'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 291\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    292\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    293\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\tensorboard\\_pytorch_graph.py\u001b[0m in \u001b[0;36mgraph\u001b[1;34m(model, args, verbose)\u001b[0m\n\u001b[0;32m    283\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0monnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_training\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# TODO: move outside of torch.onnx?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 285\u001b[1;33m             \u001b[0mtrace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    286\u001b[0m             \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_pass_inline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\jit\\__init__.py\u001b[0m in \u001b[0;36mtrace\u001b[1;34m(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, _force_outplace, _module_class, _compilation_unit)\u001b[0m\n\u001b[0;32m    880\u001b[0m         return trace_module(func, {'forward': example_inputs}, None,\n\u001b[0;32m    881\u001b[0m                             \u001b[0mcheck_trace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrap_check_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcheck_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 882\u001b[1;33m                             check_tolerance, _force_outplace, _module_class)\n\u001b[0m\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m     if (hasattr(func, '__self__') and isinstance(func.__self__, torch.nn.Module) and\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\jit\\__init__.py\u001b[0m in \u001b[0;36mtrace_module\u001b[1;34m(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, _force_outplace, _module_class, _compilation_unit)\u001b[0m\n\u001b[0;32m   1032\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmod\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod_name\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"forward\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1033\u001b[0m             \u001b[0mexample_inputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1034\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_method_from_trace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexample_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_lookup_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_force_outplace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1035\u001b[0m             \u001b[0mcheck_trace_method\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1036\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\Internship\\Pose_Estimation\\Livewire\\Pose_Estimation_LiveWire\\model.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    204\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnet_module\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0minput_img\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_img\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'bilinear'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m             \u001b[0mconfidence_maps\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\Internship\\Pose_Estimation\\Livewire\\Pose_Estimation_LiveWire\\model.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    180\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolated_layer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[1;31m#             print(x.shape)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 182\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    183\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\Internship\\Pose_Estimation\\Livewire\\Pose_Estimation_LiveWire\\model.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    149\u001b[0m         )\n\u001b[0;32m    150\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     98\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m             \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    101\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\Internship\\Pose_Estimation\\Livewire\\Pose_Estimation_LiveWire\\model.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     29\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReLU\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatchnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    528\u001b[0m                 \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_slow_forward\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mrecording_scopes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrecording_scopes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mconv2d_forward\u001b[1;34m(self, input, weight)\u001b[0m\n\u001b[0;32m    340\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m    341\u001b[0m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[1;32m--> 342\u001b[1;33m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.80 GiB already allocated; 1.27 MiB free; 2.93 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "writer.add_graph(model, images)\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch number :  1 epoch :  0  tensor size =  torch.Size([1, 3, 324, 235]) memory usage =  152963072\n",
      "batch number :  2 epoch :  0  tensor size =  torch.Size([1, 3, 365, 150]) memory usage =  466898944\n",
      "batch number :  3 epoch :  0  tensor size =  torch.Size([1, 3, 465, 347]) memory usage =  496993280\n",
      "batch number :  4 epoch :  0  tensor size =  torch.Size([1, 3, 448, 231]) memory usage =  480559104\n",
      "batch number :  5 epoch :  0  tensor size =  torch.Size([1, 3, 200, 107]) memory usage =  457246208\n",
      "batch number :  6 epoch :  0  tensor size =  torch.Size([1, 3, 170, 106]) memory usage =  456286720\n",
      "batch number :  7 epoch :  0  tensor size =  torch.Size([1, 3, 224, 78]) memory usage =  456130560\n",
      "batch number :  8 epoch :  0  tensor size =  torch.Size([1, 3, 173, 287]) memory usage =  465269760\n",
      "batch number :  9 epoch :  0  tensor size =  torch.Size([1, 3, 373, 317]) memory usage =  484749312\n",
      "batch number :  10 epoch :  0  tensor size =  torch.Size([1, 3, 408, 289]) memory usage =  484655616\n",
      "batch number :  11 epoch :  0  tensor size =  torch.Size([1, 3, 128, 112]) memory usage =  455239680\n",
      "batch number :  12 epoch :  0  tensor size =  torch.Size([1, 3, 194, 92]) memory usage =  456237568\n",
      "batch number :  13 epoch :  0  tensor size =  torch.Size([1, 3, 188, 185]) memory usage =  461046272\n",
      "batch number :  14 epoch :  0  tensor size =  torch.Size([1, 3, 432, 325]) memory usage =  491042304\n",
      "batch number :  15 epoch :  0  tensor size =  torch.Size([1, 3, 331, 199]) memory usage =  469875200\n",
      "batch number :  16 epoch :  0  tensor size =  torch.Size([1, 3, 269, 188]) memory usage =  466455552\n",
      "batch number :  17 epoch :  0  tensor size =  torch.Size([1, 3, 240, 167]) memory usage =  462551552\n",
      "batch number :  18 epoch :  0  tensor size =  torch.Size([1, 3, 189, 142]) memory usage =  458790912\n",
      "batch number :  19 epoch :  0  tensor size =  torch.Size([1, 3, 213, 86]) memory usage =  456371200\n",
      "batch number :  20 epoch :  0  tensor size =  torch.Size([1, 3, 221, 102]) memory usage =  457570816\n",
      "batch number :  21 epoch :  0  tensor size =  torch.Size([1, 3, 142, 72]) memory usage =  454072320\n",
      "batch number :  22 epoch :  0  tensor size =  torch.Size([1, 3, 170, 75]) memory usage =  454789632\n",
      "batch number :  23 epoch :  0  tensor size =  torch.Size([1, 3, 193, 91]) memory usage =  456156672\n",
      "batch number :  24 epoch :  0  tensor size =  torch.Size([1, 3, 444, 292]) memory usage =  487988736\n",
      "batch number :  25 epoch :  0  tensor size =  torch.Size([1, 3, 273, 187]) memory usage =  466461184\n",
      "batch number :  26 epoch :  0  tensor size =  torch.Size([1, 3, 262, 273]) memory usage =  471482368\n",
      "batch number :  27 epoch :  0  tensor size =  torch.Size([1, 3, 473, 323]) memory usage =  494557696\n",
      "batch number :  28 epoch :  0  tensor size =  torch.Size([1, 3, 221, 90]) memory usage =  456817664\n",
      "batch number :  29 epoch :  0  tensor size =  torch.Size([1, 3, 279, 162]) memory usage =  464293888\n",
      "batch number :  30 epoch :  0  tensor size =  torch.Size([1, 3, 330, 244]) memory usage =  474036736\n",
      "batch number :  31 epoch :  0  tensor size =  torch.Size([1, 3, 353, 217]) memory usage =  473059328\n",
      "batch number :  32 epoch :  0  tensor size =  torch.Size([1, 3, 340, 110]) memory usage =  461790208\n",
      "batch number :  33 epoch :  0  tensor size =  torch.Size([1, 3, 284, 209]) memory usage =  468025856\n",
      "batch number :  34 epoch :  0  tensor size =  torch.Size([1, 3, 354, 166]) memory usage =  467857920\n",
      "batch number :  35 epoch :  0  tensor size =  torch.Size([1, 3, 277, 440]) memory usage =  485782528\n",
      "batch number :  36 epoch :  0  tensor size =  torch.Size([1, 3, 437, 468]) memory usage =  509251584\n",
      "batch number :  37 epoch :  0  tensor size =  torch.Size([1, 3, 282, 121]) memory usage =  460859392\n",
      "batch number :  38 epoch :  0  tensor size =  torch.Size([1, 3, 309, 108]) memory usage =  460646400\n",
      "batch number :  39 epoch :  0  tensor size =  torch.Size([1, 3, 298, 381]) memory usage =  483414016\n",
      "batch number :  40 epoch :  0  tensor size =  torch.Size([1, 3, 380, 191]) memory usage =  472750080\n",
      "batch number :  41 epoch :  0  tensor size =  torch.Size([1, 3, 201, 116]) memory usage =  457790464\n",
      "batch number :  42 epoch :  0  tensor size =  torch.Size([1, 3, 253, 108]) memory usage =  458928640\n",
      "batch number :  43 epoch :  0  tensor size =  torch.Size([1, 3, 342, 179]) memory usage =  468554752\n",
      "batch number :  44 epoch :  0  tensor size =  torch.Size([1, 3, 174, 80]) memory usage =  455121920\n",
      "batch number :  45 epoch :  0  tensor size =  torch.Size([1, 3, 289, 171]) memory usage =  465203712\n",
      "batch number :  46 epoch :  0  tensor size =  torch.Size([1, 3, 305, 192]) memory usage =  467799552\n",
      "batch number :  47 epoch :  0  tensor size =  torch.Size([1, 3, 326, 321]) memory usage =  480888320\n",
      "batch number :  48 epoch :  0  tensor size =  torch.Size([1, 3, 501, 250]) memory usage =  486739968\n",
      "batch number :  49 epoch :  0  tensor size =  torch.Size([1, 3, 165, 75]) memory usage =  454683648\n",
      "batch number :  50 epoch :  0  tensor size =  torch.Size([1, 3, 199, 105]) memory usage =  457102848\n",
      "batch number :  51 epoch :  0  tensor size =  torch.Size([1, 3, 329, 175]) memory usage =  467520000\n",
      "batch number :  52 epoch :  0  tensor size =  torch.Size([1, 3, 170, 135]) memory usage =  457686528\n",
      "batch number :  53 epoch :  0  tensor size =  torch.Size([1, 3, 327, 179]) memory usage =  467791872\n",
      "batch number :  54 epoch :  0  tensor size =  torch.Size([1, 3, 331, 125]) memory usage =  462919168\n",
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     "name": "stdout",
     "output_type": "stream",
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     "name": "stdout",
     "output_type": "stream",
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      "batch number :  439 epoch :  0  tensor size =  torch.Size([1, 3, 125, 92]) memory usage =  454434816\n",
      "batch number :  440 epoch :  0  tensor size =  torch.Size([1, 3, 258, 102]) memory usage =  458642432\n",
      "batch number :  441 epoch :  0  tensor size =  torch.Size([1, 3, 147, 144]) memory usage =  457180672\n",
      "batch number :  442 epoch :  0  tensor size =  torch.Size([1, 3, 311, 142]) memory usage =  464281600\n",
      "batch number :  443 epoch :  0  tensor size =  torch.Size([1, 3, 244, 213]) memory usage =  466472448\n",
      "batch number :  444 epoch :  0  tensor size =  torch.Size([1, 3, 204, 93]) memory usage =  456556544\n",
      "batch number :  445 epoch :  0  tensor size =  torch.Size([1, 3, 146, 87]) memory usage =  454775808\n",
      "batch number :  446 epoch :  0  tensor size =  torch.Size([1, 3, 201, 152]) memory usage =  459845632\n",
      "batch number :  447 epoch :  0  tensor size =  torch.Size([1, 3, 148, 75]) memory usage =  454321152\n",
      "batch number :  448 epoch :  0  tensor size =  torch.Size([1, 3, 220, 202]) memory usage =  464284672\n",
      "batch number :  449 epoch :  0  tensor size =  torch.Size([1, 3, 113, 173]) memory usage =  456720896\n",
      "batch number :  450 epoch :  0  tensor size =  torch.Size([1, 3, 329, 239]) memory usage =  473499648\n",
      "batch number :  451 epoch :  0  tensor size =  torch.Size([1, 3, 191, 105]) memory usage =  456864768\n",
      "batch number :  452 epoch :  0  tensor size =  torch.Size([1, 3, 166, 65]) memory usage =  454233088\n",
      "batch number :  453 epoch :  0  tensor size =  torch.Size([1, 3, 193, 82]) memory usage =  455663104\n",
      "batch number :  454 epoch :  0  tensor size =  torch.Size([1, 3, 216, 109]) memory usage =  457854976\n",
      "batch number :  455 epoch :  0  tensor size =  torch.Size([1, 3, 147, 81]) memory usage =  454550528\n",
      "batch number :  456 epoch :  0  tensor size =  torch.Size([1, 3, 407, 258]) memory usage =  480990720\n",
      "batch number :  457 epoch :  0  tensor size =  torch.Size([1, 3, 232, 170]) memory usage =  462369792\n",
      "batch number :  458 epoch :  0  tensor size =  torch.Size([1, 3, 371, 178]) memory usage =  470835200\n",
      "batch number :  459 epoch :  0  tensor size =  torch.Size([1, 3, 162, 148]) memory usage =  457977856\n",
      "batch number :  460 epoch :  0  tensor size =  torch.Size([1, 3, 179, 171]) memory usage =  459862016\n",
      "batch number :  461 epoch :  0  tensor size =  torch.Size([1, 3, 154, 87]) memory usage =  454973952\n",
      "batch number :  462 epoch :  0  tensor size =  torch.Size([1, 3, 248, 123]) memory usage =  459831808\n",
      "batch number :  463 epoch :  0  tensor size =  torch.Size([1, 3, 355, 391]) memory usage =  490589696\n",
      "batch number :  464 epoch :  0  tensor size =  torch.Size([1, 3, 376, 177]) memory usage =  470841344\n",
      "batch number :  465 epoch :  0  tensor size =  torch.Size([1, 3, 363, 270]) memory usage =  479003648\n",
      "batch number :  466 epoch :  0  tensor size =  torch.Size([1, 3, 221, 91]) memory usage =  456880128\n",
      "batch number :  467 epoch :  0  tensor size =  torch.Size([1, 3, 363, 224]) memory usage =  475212800\n",
      "batch number :  468 epoch :  0  tensor size =  torch.Size([1, 3, 215, 89]) memory usage =  456603136\n",
      "batch number :  469 epoch :  0  tensor size =  torch.Size([1, 3, 457, 394]) memory usage =  502305280\n",
      "batch number :  470 epoch :  0  tensor size =  torch.Size([1, 3, 143, 76]) memory usage =  454255104\n",
      "batch number :  471 epoch :  0  tensor size =  torch.Size([1, 3, 307, 157]) memory usage =  464857088\n",
      "batch number :  472 epoch :  0  tensor size =  torch.Size([1, 3, 164, 70]) memory usage =  454429184\n",
      "batch number :  473 epoch :  0  tensor size =  torch.Size([1, 3, 221, 120]) memory usage =  458700288\n",
      "batch number :  474 epoch :  0  tensor size =  torch.Size([1, 3, 416, 376]) memory usage =  495590400\n",
      "batch number :  475 epoch :  0  tensor size =  torch.Size([1, 3, 223, 102]) memory usage =  457628672\n",
      "batch number :  476 epoch :  0  tensor size =  torch.Size([1, 3, 189, 102]) memory usage =  456643584\n",
      "batch number :  477 epoch :  0  tensor size =  torch.Size([1, 3, 275, 343]) memory usage =  477957120\n",
      "batch number :  478 epoch :  0  tensor size =  torch.Size([1, 3, 364, 185]) memory usage =  470851072\n",
      "batch number :  479 epoch :  0  tensor size =  torch.Size([1, 3, 315, 113]) memory usage =  461277696\n",
      "batch number :  480 epoch :  0  tensor size =  torch.Size([1, 3, 420, 151]) memory usage =  469179904\n",
      "batch number :  481 epoch :  0  tensor size =  torch.Size([1, 3, 275, 158]) memory usage =  464272896\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch number :  482 epoch :  0  tensor size =  torch.Size([1, 3, 257, 169]) memory usage =  464272384\n",
      "batch number :  483 epoch :  0  tensor size =  torch.Size([1, 3, 154, 89]) memory usage =  455061504\n",
      "batch number :  484 epoch :  0  tensor size =  torch.Size([1, 3, 134, 98]) memory usage =  454898176\n",
      "batch number :  485 epoch :  0  tensor size =  torch.Size([1, 3, 174, 210]) memory usage =  461545984\n",
      "batch number :  486 epoch :  0  tensor size =  torch.Size([1, 3, 411, 186]) memory usage =  473057280\n",
      "batch number :  487 epoch :  0  tensor size =  torch.Size([1, 3, 190, 80]) memory usage =  455485440\n",
      "batch number :  488 epoch :  0  tensor size =  torch.Size([1, 3, 184, 74]) memory usage =  455035904\n",
      "batch number :  489 epoch :  0  tensor size =  torch.Size([1, 3, 201, 78]) memory usage =  455621120\n",
      "batch number :  490 epoch :  0  tensor size =  torch.Size([1, 3, 361, 162]) memory usage =  467777536\n",
      "batch number :  491 epoch :  0  tensor size =  torch.Size([1, 3, 119, 103]) memory usage =  454649856\n",
      "batch number :  492 epoch :  0  tensor size =  torch.Size([1, 3, 427, 170]) memory usage =  472750080\n",
      "batch number :  493 epoch :  0  tensor size =  torch.Size([1, 3, 453, 197]) memory usage =  476513280\n",
      "batch number :  494 epoch :  0  tensor size =  torch.Size([1, 3, 236, 94]) memory usage =  457468928\n",
      "batch number :  495 epoch :  0  tensor size =  torch.Size([1, 3, 149, 81]) memory usage =  454596096\n",
      "batch number :  496 epoch :  0  tensor size =  torch.Size([1, 3, 389, 168]) memory usage =  469728768\n",
      "batch number :  497 epoch :  0  tensor size =  torch.Size([1, 3, 215, 103]) memory usage =  457458176\n",
      "batch number :  498 epoch :  0  tensor size =  torch.Size([1, 3, 297, 237]) memory usage =  471159296\n",
      "batch number :  499 epoch :  0  tensor size =  torch.Size([1, 3, 199, 76]) memory usage =  455463936\n",
      "val batch number :  0  tensor size =  torch.Size([3, 3, 404, 341]) memory usage =  568543744\n",
      "val batch number :  1  tensor size =  torch.Size([5, 3, 474, 513]) memory usage =  796549632\n",
      "val batch number :  2  tensor size =  torch.Size([2, 3, 329, 175]) memory usage =  483871744\n",
      "val batch number :  3  tensor size =  torch.Size([5, 3, 543, 333]) memory usage =  708283904\n",
      "val batch number :  4  tensor size =  torch.Size([7, 3, 368, 377]) memory usage =  727905280\n",
      "val batch number :  5  tensor size =  torch.Size([4, 3, 481, 366]) memory usage =  651157504\n",
      "val batch number :  6  tensor size =  torch.Size([7, 3, 371, 231]) memory usage =  621542912\n",
      "val batch number :  7  tensor size =  torch.Size([5, 3, 334, 242]) memory usage =  566478848\n",
      "val batch number :  8  tensor size =  torch.Size([1, 3, 245, 125]) memory usage =  459866624\n",
      "val batch number :  9  tensor size =  torch.Size([4, 3, 261, 190]) memory usage =  507503616\n",
      "val batch number :  10  tensor size =  torch.Size([4, 3, 407, 354]) memory usage =  614841856\n",
      "val batch number :  11  tensor size =  torch.Size([5, 3, 351, 399]) memory usage =  650038784\n",
      "val batch number :  12  tensor size =  torch.Size([5, 3, 399, 207]) memory usage =  568451072\n",
      "val batch number :  13  tensor size =  torch.Size([1, 3, 340, 164]) memory usage =  467004928\n",
      "val batch number :  14  tensor size =  torch.Size([1, 3, 289, 138]) memory usage =  462495744\n",
      "val batch number :  15  tensor size =  torch.Size([1, 3, 410, 120]) memory usage =  465142272\n",
      "val batch number :  16  tensor size =  torch.Size([2, 3, 516, 303]) memory usage =  539974656\n",
      "val batch number :  17  tensor size =  torch.Size([2, 3, 366, 293]) memory usage =  512080384\n",
      "val batch number :  18  tensor size =  torch.Size([3, 3, 280, 131]) memory usage =  482420736\n",
      "val batch number :  19  tensor size =  torch.Size([4, 3, 265, 125]) memory usage =  488799232\n",
      "val batch number :  20  tensor size =  torch.Size([1, 3, 251, 147]) memory usage =  461647872\n",
      "val batch number :  21  tensor size =  torch.Size([2, 3, 419, 301]) memory usage =  522804736\n",
      "val batch number :  22  tensor size =  torch.Size([1, 3, 188, 73]) memory usage =  455066624\n",
      "val batch number :  23  tensor size =  torch.Size([2, 3, 196, 113]) memory usage =  464283648\n",
      "val batch number :  24  tensor size =  torch.Size([3, 3, 452, 352]) memory usage =  586725376\n",
      "val batch number :  25  tensor size =  torch.Size([6, 3, 593, 523]) memory usage =  979646464\n",
      "val batch number :  26  tensor size =  torch.Size([5, 3, 495, 396]) memory usage =  729518080\n",
      "val batch number :  27  tensor size =  torch.Size([7, 3, 559, 416]) memory usage =  913466368\n",
      "val batch number :  28  tensor size =  torch.Size([7, 3, 443, 501]) memory usage =  892391936\n",
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      "val batch number :  31  tensor size =  torch.Size([7, 3, 620, 396]) memory usage =  939349504\n",
      "val batch number :  32  tensor size =  torch.Size([5, 3, 372, 255]) memory usage =  585870336\n",
      "val batch number :  33  tensor size =  torch.Size([7, 3, 508, 356]) memory usage =  810695168\n",
      "val batch number :  34  tensor size =  torch.Size([5, 3, 633, 386]) memory usage =  798541824\n",
      "val batch number :  35  tensor size =  torch.Size([6, 3, 575, 360]) memory usage =  803897344\n",
      "val batch number :  36  tensor size =  torch.Size([7, 3, 383, 307]) memory usage =  684920320\n",
      "val batch number :  37  tensor size =  torch.Size([5, 3, 418, 425]) memory usage =  703432192\n",
      "val batch number :  38  tensor size =  torch.Size([5, 3, 506, 275]) memory usage =  648762368\n",
      "val batch number :  39  tensor size =  torch.Size([3, 3, 496, 433]) memory usage =  634151424\n",
      "val batch number :  40  tensor size =  torch.Size([1, 3, 226, 177]) memory usage =  462530048\n",
      "val batch number :  41  tensor size =  torch.Size([7, 3, 405, 373]) memory usage =  751486464\n",
      "val batch number :  42  tensor size =  torch.Size([5, 3, 410, 333]) memory usage =  645041664\n",
      "val batch number :  43  tensor size =  torch.Size([1, 3, 536, 249]) memory usage =  489073152\n",
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      "val batch number :  46  tensor size =  torch.Size([4, 3, 284, 195]) memory usage =  514080768\n",
      "val batch number :  47  tensor size =  torch.Size([2, 3, 305, 153]) memory usage =  477675008\n",
      "val batch number :  48  tensor size =  torch.Size([1, 3, 227, 123]) memory usage =  459099136\n",
      "val batch number :  49  tensor size =  torch.Size([4, 3, 391, 350]) memory usage =  607540224\n",
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      "val batch number :  51  tensor size =  torch.Size([2, 3, 340, 148]) memory usage =  479750656\n",
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      "val batch number :  53  tensor size =  torch.Size([1, 3, 197, 151]) memory usage =  459617792\n",
      "val batch number :  54  tensor size =  torch.Size([2, 3, 301, 571]) memory usage =  548791808\n",
      "val batch number :  55  tensor size =  torch.Size([2, 3, 167, 106]) memory usage =  461223936\n",
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      "val batch number :  57  tensor size =  torch.Size([2, 3, 275, 114]) memory usage =  468976128\n",
      "val batch number :  58  tensor size =  torch.Size([5, 3, 314, 247]) memory usage =  561301504\n",
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      "val batch number :  76  tensor size =  torch.Size([2, 3, 303, 226]) memory usage =  490064384\n",
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      "val batch number :  100  tensor size =  torch.Size([2, 3, 447, 295]) memory usage =  526068736\n",
      "batch number :  500 epoch :  0  tensor size =  torch.Size([1, 3, 269, 108]) memory usage =  459419648\n",
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     "name": "stdout",
     "output_type": "stream",
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      "batch number :  918 epoch :  0  tensor size =  torch.Size([1, 3, 251, 148]) memory usage =  461719040\n",
      "batch number :  919 epoch :  0  tensor size =  torch.Size([1, 3, 250, 326]) memory usage =  474315264\n",
      "batch number :  920 epoch :  0  tensor size =  torch.Size([1, 3, 244, 182]) memory usage =  463780864\n",
      "batch number :  921 epoch :  0  tensor size =  torch.Size([1, 3, 309, 169]) memory usage =  466475520\n",
      "batch number :  922 epoch :  0  tensor size =  torch.Size([1, 3, 205, 95]) memory usage =  456700416\n",
      "batch number :  923 epoch :  0  tensor size =  torch.Size([1, 3, 228, 150]) memory usage =  460881920\n",
      "batch number :  924 epoch :  0  tensor size =  torch.Size([1, 3, 185, 107]) memory usage =  456791040\n",
      "batch number :  925 epoch :  0  tensor size =  torch.Size([1, 3, 268, 239]) memory usage =  469360128\n",
      "batch number :  926 epoch :  0  tensor size =  torch.Size([1, 3, 369, 138]) memory usage =  466460160\n",
      "batch number :  927 epoch :  0  tensor size =  torch.Size([1, 3, 341, 190]) memory usage =  469569536\n",
      "batch number :  928 epoch :  0  tensor size =  torch.Size([1, 3, 98, 111]) memory usage =  454258176\n",
      "batch number :  929 epoch :  0  tensor size =  torch.Size([1, 3, 190, 219]) memory usage =  462986752\n",
      "batch number :  930 epoch :  0  tensor size =  torch.Size([1, 3, 171, 78]) memory usage =  454957056\n",
      "batch number :  931 epoch :  0  tensor size =  torch.Size([1, 3, 357, 237]) memory usage =  475198464\n",
      "batch number :  932 epoch :  0  tensor size =  torch.Size([1, 3, 192, 83]) memory usage =  455694848\n",
      "batch number :  933 epoch :  0  tensor size =  torch.Size([1, 3, 492, 344]) memory usage =  499235328\n",
      "batch number :  934 epoch :  0  tensor size =  torch.Size([1, 3, 286, 100]) memory usage =  459291648\n",
      "batch number :  935 epoch :  0  tensor size =  torch.Size([1, 3, 81, 132]) memory usage =  454205952\n",
      "batch number :  936 epoch :  0  tensor size =  torch.Size([1, 3, 258, 105]) memory usage =  458862592\n",
      "batch number :  937 epoch :  0  tensor size =  torch.Size([1, 3, 472, 317]) memory usage =  494002176\n",
      "batch number :  938 epoch :  0  tensor size =  torch.Size([1, 3, 459, 192]) memory usage =  476197376\n",
      "batch number :  939 epoch :  0  tensor size =  torch.Size([1, 3, 182, 91]) memory usage =  455873024\n",
      "batch number :  940 epoch :  0  tensor size =  torch.Size([1, 3, 220, 82]) memory usage =  456292352\n",
      "batch number :  941 epoch :  0  tensor size =  torch.Size([1, 3, 505, 360]) memory usage =  502800384\n",
      "batch number :  942 epoch :  0  tensor size =  torch.Size([1, 3, 259, 130]) memory usage =  460731904\n",
      "batch number :  943 epoch :  0  tensor size =  torch.Size([1, 3, 244, 115]) memory usage =  459138048\n",
      "batch number :  944 epoch :  0  tensor size =  torch.Size([1, 3, 299, 146]) memory usage =  463567360\n",
      "batch number :  945 epoch :  0  tensor size =  torch.Size([1, 3, 306, 257]) memory usage =  473503744\n",
      "batch number :  946 epoch :  0  tensor size =  torch.Size([1, 3, 537, 194]) memory usage =  480755712\n",
      "batch number :  947 epoch :  0  tensor size =  torch.Size([1, 3, 167, 133]) memory usage =  457477120\n",
      "batch number :  948 epoch :  0  tensor size =  torch.Size([1, 3, 319, 334]) memory usage =  481428480\n",
      "batch number :  949 epoch :  0  tensor size =  torch.Size([1, 3, 379, 261]) memory usage =  479262208\n",
      "batch number :  950 epoch :  0  tensor size =  torch.Size([1, 3, 375, 491]) memory usage =  503460864\n",
      "batch number :  951 epoch :  0  tensor size =  torch.Size([1, 3, 341, 358]) memory usage =  485839360\n",
      "batch number :  952 epoch :  0  tensor size =  torch.Size([1, 3, 335, 433]) memory usage =  492364800\n",
      "batch number :  953 epoch :  0  tensor size =  torch.Size([1, 3, 187, 55]) memory usage =  454090240\n",
      "batch number :  954 epoch :  0  tensor size =  torch.Size([1, 3, 356, 121]) memory usage =  463403008\n",
      "batch number :  955 epoch :  0  tensor size =  torch.Size([1, 3, 321, 173]) memory usage =  466940416\n",
      "batch number :  956 epoch :  0  tensor size =  torch.Size([1, 3, 278, 260]) memory usage =  471696896\n",
      "batch number :  957 epoch :  0  tensor size =  torch.Size([1, 3, 243, 110]) memory usage =  458760704\n",
      "batch number :  958 epoch :  0  tensor size =  torch.Size([1, 3, 154, 115]) memory usage =  456199168\n",
      "batch number :  959 epoch :  0  tensor size =  torch.Size([1, 3, 157, 91]) memory usage =  455226368\n",
      "batch number :  960 epoch :  0  tensor size =  torch.Size([1, 3, 245, 183]) memory usage =  463902208\n",
      "batch number :  961 epoch :  0  tensor size =  torch.Size([1, 3, 162, 121]) memory usage =  456736256\n",
      "batch number :  962 epoch :  0  tensor size =  torch.Size([1, 3, 352, 198]) memory usage =  470962688\n",
      "batch number :  963 epoch :  0  tensor size =  torch.Size([1, 3, 426, 400]) memory usage =  499562496\n",
      "batch number :  964 epoch :  0  tensor size =  torch.Size([1, 3, 133, 104]) memory usage =  455097856\n",
      "batch number :  965 epoch :  0  tensor size =  torch.Size([1, 3, 303, 194]) memory usage =  468651520\n",
      "batch number :  966 epoch :  0  tensor size =  torch.Size([1, 3, 344, 235]) memory usage =  474127872\n",
      "batch number :  967 epoch :  0  tensor size =  torch.Size([1, 3, 432, 274]) memory usage =  484785664\n",
      "batch number :  968 epoch :  0  tensor size =  torch.Size([1, 3, 202, 86]) memory usage =  456102912\n",
      "batch number :  969 epoch :  0  tensor size =  torch.Size([1, 3, 193, 474]) memory usage =  477150208\n",
      "batch number :  970 epoch :  0  tensor size =  torch.Size([1, 3, 503, 392]) memory usage =  507167232\n",
      "batch number :  971 epoch :  0  tensor size =  torch.Size([1, 3, 402, 298]) memory usage =  485191168\n",
      "batch number :  972 epoch :  0  tensor size =  torch.Size([1, 3, 191, 148]) memory usage =  459197440\n",
      "batch number :  973 epoch :  0  tensor size =  torch.Size([1, 3, 220, 245]) memory usage =  466496000\n",
      "batch number :  974 epoch :  0  tensor size =  torch.Size([1, 3, 256, 188]) memory usage =  464837120\n",
      "batch number :  975 epoch :  0  tensor size =  torch.Size([1, 3, 343, 136]) memory usage =  464417280\n",
      "batch number :  976 epoch :  0  tensor size =  torch.Size([1, 3, 125, 92]) memory usage =  454435328\n",
      "batch number :  977 epoch :  0  tensor size =  torch.Size([1, 3, 447, 287]) memory usage =  487603200\n",
      "batch number :  978 epoch :  0  tensor size =  torch.Size([1, 3, 208, 99]) memory usage =  457017344\n",
      "batch number :  979 epoch :  0  tensor size =  torch.Size([1, 3, 540, 279]) memory usage =  494014976\n",
      "batch number :  980 epoch :  0  tensor size =  torch.Size([1, 3, 414, 416]) memory usage =  500080640\n",
      "batch number :  981 epoch :  0  tensor size =  torch.Size([1, 3, 173, 91]) memory usage =  455640064\n",
      "batch number :  982 epoch :  0  tensor size =  torch.Size([1, 3, 444, 386]) memory usage =  499842048\n",
      "batch number :  983 epoch :  0  tensor size =  torch.Size([1, 3, 166, 113]) memory usage =  456496640\n",
      "batch number :  984 epoch :  0  tensor size =  torch.Size([1, 3, 219, 142]) memory usage =  460000768\n",
      "batch number :  985 epoch :  0  tensor size =  torch.Size([1, 3, 325, 128]) memory usage =  462983168\n",
      "batch number :  986 epoch :  0  tensor size =  torch.Size([1, 3, 453, 257]) memory usage =  484232704\n",
      "batch number :  987 epoch :  0  tensor size =  torch.Size([1, 3, 445, 369]) memory usage =  497803264\n",
      "batch number :  988 epoch :  0  tensor size =  torch.Size([1, 3, 392, 261]) memory usage =  480225792\n",
      "batch number :  989 epoch :  0  tensor size =  torch.Size([1, 3, 329, 197]) memory usage =  469576192\n",
      "batch number :  990 epoch :  0  tensor size =  torch.Size([1, 3, 326, 216]) memory usage =  471167488\n",
      "batch number :  991 epoch :  0  tensor size =  torch.Size([1, 3, 133, 129]) memory usage =  456041984\n",
      "batch number :  992 epoch :  0  tensor size =  torch.Size([1, 3, 139, 96]) memory usage =  454958592\n",
      "batch number :  993 epoch :  0  tensor size =  torch.Size([1, 3, 274, 161]) memory usage =  463697408\n",
      "batch number :  994 epoch :  0  tensor size =  torch.Size([1, 3, 382, 228]) memory usage =  475904512\n",
      "batch number :  995 epoch :  0  tensor size =  torch.Size([1, 3, 231, 178]) memory usage =  462846464\n",
      "batch number :  996 epoch :  0  tensor size =  torch.Size([1, 3, 329, 264]) memory usage =  475836416\n",
      "batch number :  997 epoch :  0  tensor size =  torch.Size([1, 3, 157, 187]) memory usage =  459507200\n",
      "batch number :  998 epoch :  0  tensor size =  torch.Size([1, 3, 261, 136]) memory usage =  461250048\n",
      "batch number :  999 epoch :  0  tensor size =  torch.Size([1, 3, 435, 321]) memory usage =  490825728\n",
      "val batch number :  0  tensor size =  torch.Size([3, 3, 418, 353]) memory usage =  576884736\n",
      "val batch number :  1  tensor size =  torch.Size([5, 3, 473, 513]) memory usage =  795851776\n",
      "val batch number :  2  tensor size =  torch.Size([1, 3, 321, 152]) memory usage =  465026048\n",
      "val batch number :  3  tensor size =  torch.Size([5, 3, 553, 351]) memory usage =  726795776\n",
      "val batch number :  4  tensor size =  torch.Size([7, 3, 361, 399]) memory usage =  737519104\n",
      "val batch number :  5  tensor size =  torch.Size([3, 3, 478, 362]) memory usage =  598596096\n",
      "val batch number :  6  tensor size =  torch.Size([8, 3, 369, 235]) memory usage =  648311296\n",
      "val batch number :  7  tensor size =  torch.Size([5, 3, 338, 241]) memory usage =  566839296\n",
      "val batch number :  8  tensor size =  torch.Size([1, 3, 246, 126]) memory usage =  459972096\n",
      "val batch number :  9  tensor size =  torch.Size([4, 3, 255, 204]) memory usage =  510263808\n",
      "val batch number :  10  tensor size =  torch.Size([5, 3, 396, 360]) memory usage =  653604352\n",
      "val batch number :  11  tensor size =  torch.Size([5, 3, 367, 401]) memory usage =  660146688\n",
      "val batch number :  12  tensor size =  torch.Size([5, 3, 410, 232]) memory usage =  586381824\n",
      "val batch number :  13  tensor size =  torch.Size([1, 3, 355, 202]) memory usage =  471534592\n",
      "val batch number :  14  tensor size =  torch.Size([1, 3, 285, 124]) memory usage =  461206016\n",
      "val batch number :  15  tensor size =  torch.Size([1, 3, 407, 129]) memory usage =  466479104\n",
      "val batch number :  16  tensor size =  torch.Size([2, 3, 507, 274]) memory usage =  530074624\n",
      "val batch number :  17  tensor size =  torch.Size([2, 3, 407, 346]) memory usage =  531156480\n",
      "val batch number :  18  tensor size =  torch.Size([3, 3, 274, 130]) memory usage =  481517568\n",
      "val batch number :  19  tensor size =  torch.Size([4, 3, 260, 108]) memory usage =  483274240\n",
      "val batch number :  20  tensor size =  torch.Size([2, 3, 251, 159]) memory usage =  473837568\n",
      "val batch number :  21  tensor size =  torch.Size([2, 3, 405, 276]) memory usage =  514660352\n",
      "val batch number :  22  tensor size =  torch.Size([1, 3, 185, 57]) memory usage =  454164480\n",
      "val batch number :  23  tensor size =  torch.Size([3, 3, 191, 103]) memory usage =  468654592\n",
      "val batch number :  24  tensor size =  torch.Size([3, 3, 467, 368]) memory usage =  597590528\n",
      "val batch number :  25  tensor size =  torch.Size([5, 3, 612, 489]) memory usage =  876129792\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-bf3108b39f1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m                 \u001b[0mvalid_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrgt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mval_dataloader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m                     \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m                         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    383\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\Internship\\Pose_Estimation\\Livewire\\Pose_Estimation_LiveWire\\Data_Loader.py\u001b[0m in \u001b[0;36mmy_collate\u001b[1;34m(Batch)\u001b[0m\n\u001b[0;32m    118\u001b[0m         \u001b[0mmini_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtop_padding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtop_padding\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mleft_padding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_padding\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'target'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m             \u001b[0mmini_batch_target\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m17\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtop_padding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtop_padding\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mleft_padding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_padding\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[0mi\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#train the neural network finally\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)\n",
    "loss_func = torch.nn.MSELoss(reduction = 'sum')\n",
    "\n",
    "i = 0\n",
    "prev_epoch = 0\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    prev_epoch += i\n",
    "    i = 0\n",
    "    for images, target in train_dataloader:\n",
    "        i += 1\n",
    "        if(images.shape[0] == 0 or images.shape[2] > 550 or images.shape[3] > 550):\n",
    "            i -= 1\n",
    "            continue\n",
    "        print(\"batch number : \",i, \"epoch : \", epoch, \" tensor size = \", images.shape, \"memory usage = \", torch.cuda.memory_allocated())\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(images)\n",
    "#         loss = 25*loss_func(pred[:, 0:17], target[:, 0:17]) + 150*loss_func(pred[:, 17:34], target[:, 17:34]) + 500*loss_func(pred[:, 34:51], target[:, 34:51]) +  1000*loss_func(pred[:, 51:68], target[:, 51:68])\n",
    "        loss = (loss_func(pred, target*((images.shape[2]*images.shape[3]) / 6400)))\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        writer.add_scalar('train_loss', loss.item(), prev_epoch + i)\n",
    "#         running_train_loss += loss.cpu().detach().item()\n",
    "        \n",
    "        del pred, images, target, loss\n",
    "        \n",
    "        \n",
    "        # write train, val losses to tensorboard\n",
    "        if i%1000 == 999:\n",
    "            \n",
    "\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                k = 0\n",
    "                valid_loss = 0\n",
    "                for img, trgt in val_dataloader:\n",
    "                    if(k > 100):\n",
    "                        break\n",
    "                    if(img.shape[0] == 0):\n",
    "                        continue\n",
    "                    print(\"val batch number : \",k, \" tensor size = \", img.shape, \"memory usage = \", torch.cuda.memory_allocated())\n",
    "                    valid_loss += loss_func(model(img), trgt)* ((img.shape[2]*img.shape[3]) / 6400)\n",
    "                    k += 1\n",
    "                    \n",
    "            writer.add_scalar('val_loss', valid_loss/k,  prev_epoch + i)\n",
    "            model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished training!!\n"
     ]
    }
   ],
   "source": [
    "torch.save(model.state_dict(), \"trained_models/first_model_3.pth\")\n",
    "print(\"finished training!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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