2_Food_detect_pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Food detect pytorch.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6SNehI0BohY",
        "colab_type": "text"
      },
      "source": [
        "**FOOD DETECTION USING  PYTORCH (ATTEMPT - 2)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiXF9gmQdr7J",
        "colab_type": "code",
        "outputId": "ee36ca11-7257-4c02-a524-4ac3f0504364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZR7-H9H7Bvhc",
        "colab_type": "text"
      },
      "source": [
        "Necessary header files were included."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-hUzbyYdkqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from engine import train_one_epoch, evaluate\n",
        "import utils\n",
        "import transforms as T\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision\n",
        "import torchvision.transforms.functional as F\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        "import torch.utils.data\n",
        "from PIL import Image, ImageFile\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjU2PHInB1PR",
        "colab_type": "text"
      },
      "source": [
        "A function to perform tensor transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cErgHdavH0i-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_transform():\n",
        "    transforms = []\n",
        "    transforms.append(T.ToTensor())\n",
        "    return T.Compose(transforms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nH-jMB9QB9qM",
        "colab_type": "text"
      },
      "source": [
        "A **Pretrained ResNet based Faster RCNN** model was obtained.\n",
        "The idea behind this attempt was to use a pretrained model on object detection and  to fine tune it for the task of food detection. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qz1oVLJ4d0uK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_instance_segmentation_model(num_classes):\n",
        "    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n",
        "    in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
        "    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAt9F-xtdsCK",
        "colab_type": "text"
      },
      "source": [
        "A Dataset class was bulit using the Pytorch libraries to generate batches of the image along with its bounding box and class informations iteratively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUfnUL98d4IL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import collections\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class OpenDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, height=512, width=512, transforms=None):\n",
        "        self.transforms = transforms\n",
        "        imgs_path=[]\n",
        "        with open(\"drive//My Drive//food images//images.txt\",'r') as f:\n",
        "          for i in f.readlines():\n",
        "            imgs_path.append(i[:-1])\n",
        "        self.imgs_path=imgs_path\n",
        "        bb=[]\n",
        "        with open(\"drive//My Drive//food images//bb_inf.txt\",'r') as f:\n",
        "          for i in f.readlines():\n",
        "            #print(eval(i)[0])\n",
        "            bb.append(eval(i))\n",
        "        self.bb=bb\n",
        "        class_names=open(\"drive//My Drive//food images//classes.txt\",'r').readlines()\n",
        "        self.class_names=[class_names[i][:-1] for i in range(len(class_names))]\n",
        "        self.height = height\n",
        "        self.width = width\n",
        "        \n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = \"drive//My Drive//food images//\"+self.imgs_path[idx]\n",
        "        img = Image.open(img_path).convert(\"RGB\")\n",
        "        #img = img.resize((self.width, self.height), resample=Image.BILINEAR)\n",
        "        \n",
        "        # processing part and extraction of boxes is left as an exercise to the reader\n",
        "        boxes=[]\n",
        "        labels=torch.zeros((len(self.bb[idx][1])))\n",
        "        a=[]\n",
        "        i=0\n",
        "        for b in self.bb[idx][1]:\n",
        "          labels[i]=int(b[0])-1\n",
        "          xmin=(b[1]-b[3]/2)*512/800\n",
        "          ymin=(b[2]-b[4]/2)*512/600\n",
        "          xmax=(b[1]+b[3]/2)*512/800\n",
        "          ymax=(b[2]+b[4]/2)*512/600\n",
        "          a.append(b[3]*b[4])\n",
        "          boxes.append([xmin,ymin,xmax,ymax])\n",
        "          i+=1\n",
        "        target = {}\n",
        "        target[\"boxes\"] = torch.as_tensor(boxes, dtype=torch.float32)\n",
        "        target[\"labels\"] = labels\n",
        "        target[\"image_id\"] = torch.tensor([idx])\n",
        "        target[\"area\"]=torch.as_tensor(a,dtype=torch.float32)\n",
        "        target[\"iscrowd\"]=torch.zeros((len(self.bb[idx][1])))\n",
        "        target=[target]\n",
        "        #img=[img]\n",
        "        if self.transforms is not None:\n",
        "            img, target = self.transforms(img, target)\n",
        "\n",
        "        return img, target\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxHa3SJIDGlu",
        "colab_type": "text"
      },
      "source": [
        "A function to plot the image along with its bounding box was defined."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uLseYMs0b10",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_bb(img, targ):\n",
        "  fig,ax=plt.subplots(1)\n",
        "  img=img.permute(1,2,0).numpy()\n",
        "  #print(img.shape)\n",
        "  ax.imshow(img)\n",
        "  for i in range(len(targ[\"boxes\"])):\n",
        "    lab=int(targ[\"labels\"][i])\n",
        "    b=targ[\"boxes\"][i]\n",
        "    x,y=b[0],b[1]\n",
        "    w,h=b[2]-b[0],b[3]-b[1]\n",
        "    bb=patches.Rectangle((x,y),w,h,edgecolor='b',fill=False)\n",
        "    ax.add_patch(bb)\n",
        "    plt.text(x,y,lab,fontsize=15,color='b')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovowjqT0DMDV",
        "colab_type": "text"
      },
      "source": [
        "Sample images from the Dataset class were used to plot images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUQ3df_HIu9S",
        "colab_type": "code",
        "outputId": "9a617c6c-b99b-44ee-c2e5-e848cf9bb36b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "data=OpenDataset(transforms=get_transform())\n",
        "img,targ=next(iter(data))\n",
        "print(img.size())\n",
        "print([t for t in targ])\n",
        "print(targ[0][\"boxes\"].size())\n",
        "print(targ[0][\"labels\"].size())\n",
        "print(targ[0][\"image_id\"].size())\n",
        "print(targ[0][\"area\"].size())\n",
        "print(targ[0][\"iscrowd\"].size())\n",
        "plot_bb(img,targ[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3, 512, 512])\n",
            "[{'boxes': tensor([[  0.0000, 122.0267, 236.8000, 414.7200],\n",
            "        [232.3200,  77.6533, 512.0000, 477.8667]]), 'labels': tensor([ 0., 41.]), 'image_id': tensor([0]), 'area': tensor([126910., 204953.]), 'iscrowd': tensor([0., 0.])}]\n",
            "torch.Size([2, 4])\n",
            "torch.Size([2])\n",
            "torch.Size([1])\n",
            "torch.Size([2])\n",
            "torch.Size([2])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv3x0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "251X8Q4HDjbE",
        "colab_type": "text"
      },
      "source": [
        "A function to train the pretrained model of **Faster RCNN**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQwUwjSbQYRg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):\n",
        "    model.train()\n",
        "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
        "    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n",
        "    header = 'Epoch: [{}]'.format(epoch)\n",
        "\n",
        "    lr_scheduler = None\n",
        "    if epoch == 0:\n",
        "        warmup_factor = 1. / 1000\n",
        "        warmup_iters = min(1000, len(data_loader) - 1)\n",
        "\n",
        "        lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor)\n",
        "\n",
        "    for images, targets in metric_logger.log_every(data_loader, print_freq, header):\n",
        "        images = list(images.to(device))\n",
        "        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n",
        "\n",
        "        loss_dict = model(images, targets)\n",
        "\n",
        "        losses = sum(loss for loss in loss_dict.values())\n",
        "\n",
        "        # reduce losses over all GPUs for logging purposes\n",
        "        loss_dict_reduced = utils.reduce_dict(loss_dict)\n",
        "        losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n",
        "\n",
        "        loss_value = losses_reduced.item()\n",
        "\n",
        "        if not math.isfinite(loss_value):\n",
        "            print(\"Loss is {}, stopping training\".format(loss_value))\n",
        "            print(loss_dict_reduced)\n",
        "            sys.exit(1)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        losses.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if lr_scheduler is not None:\n",
        "            lr_scheduler.step()\n",
        "\n",
        "        metric_logger.update(loss=losses_reduced, **loss_dict_reduced)\n",
        "        metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBzNOSOaDus4",
        "colab_type": "text"
      },
      "source": [
        "This attempted failed due to an error which occured continuously."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TccZvCYYeIit",
        "colab_type": "code",
        "outputId": "85ab73f5-05ea-4237-8891-7b68f7db510b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "num_classes = 100\n",
        "device = torch.device('cuda:0')\n",
        "dataset_train = OpenDataset(transforms=get_transform())\n",
        "\n",
        "model_ft = get_instance_segmentation_model(num_classes)\n",
        "model_ft.to(device)\n",
        "\n",
        "data_loader = torch.utils.data.DataLoader(\n",
        "    dataset_train, batch_size=1, shuffle=True)\n",
        "\n",
        "params = [p for p in model_ft.parameters() if p.requires_grad]\n",
        "optimizer = torch.optim.SGD(params, lr=0.005,\n",
        "                            momentum=0.9, weight_decay=0.0005)\n",
        "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
        "                                               step_size=5,\n",
        "                                               gamma=0.1)\n",
        "num_epochs = 100\n",
        "for epoch in range(num_epochs):\n",
        "    train_one_epoch(model_ft, optimizer, data_loader, device, epoch, print_freq=1)\n",
        "    lr_scheduler.step()\n",
        "\n",
        "#torch.save(model_ft.state_dict(), \"model.bin\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-495dd8be57f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mnum_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_ft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/engine.py\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, optimizer, data_loader, device, epoch, print_freq)\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mloss_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloss_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/generalized_rcnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, images, targets)\u001b[0m\n\u001b[1;32m     45\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"In training mode, targets should be passed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0moriginal_image_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m         \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackbone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, images, targets)\u001b[0m\n\u001b[1;32m     38\u001b[0m                                  \"of shape [C, H, W], got {}\".format(image.shape))\n\u001b[1;32m     39\u001b[0m             \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m             \u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m             \u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtargets\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(self, image, target)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"boxes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresize_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mtarget\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"boxes\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py\u001b[0m in \u001b[0;36mresize_boxes\u001b[0;34m(boxes, original_size, new_size)\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0mratios\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_orig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_orig\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0mratio_height\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mratio_width\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mratios\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m     \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m     \u001b[0mxmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxmin\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mratio_width\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0mxmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxmax\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mratio_width\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 3)"
          ]
        }
      ]
    }
  ]
}