Spatial_Stream_Full.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tgqAhYHvNg2"
      },
      "source": [
        "# Check GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA580dbFbyEn",
        "outputId": "478fcce9-448d-4e1c-f3c6-9aeabe42838c"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Dec  3 04:37:19 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOC44nKYDOn8"
      },
      "source": [
        "# Run this command once and restart the runtime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhZffaB6C7Z7",
        "outputId": "53b9305b-7e4b-4d41-ed56-24fc99fe419b"
      },
      "source": [
        "!pip install av"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting av\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/62/9a992be76f8e13ce0e3a24a838191b546805545116f9fc869bd11bd21b5f/av-8.0.2-cp36-cp36m-manylinux2010_x86_64.whl (36.9MB)\n",
            "\u001b[K     |████████████████████████████████| 36.9MB 132kB/s \n",
            "\u001b[?25hInstalling collected packages: av\n",
            "Successfully installed av-8.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ati1HrCZNHE_"
      },
      "source": [
        "# HMDB51 + DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQwd2BDyLFxT"
      },
      "source": [
        "import glob\n",
        "import os\n",
        "\n",
        "from torchvision.datasets.utils import list_dir\n",
        "from torchvision.datasets.folder import make_dataset\n",
        "from torchvision.datasets.video_utils import VideoClips\n",
        "from torchvision.datasets.vision import VisionDataset\n",
        "\n",
        "class HMDB51(VisionDataset):\n",
        "    \"\"\"\n",
        "    Internally, it uses a VideoClips object to handle clip creation.\n",
        "    Args:\n",
        "        root (string): Root directory of the HMDB51 Dataset.\n",
        "        frames_per_clip (int): Number of frames in a clip.\n",
        "        step_between_clips (int): Number of frames between each clip.\n",
        "        train (bool, optional): If ``True``, creates a dataset from the train split,\n",
        "            otherwise from the ``test`` split.\n",
        "        transform (callable, optional): A function/transform that takes in a TxHxWxC video\n",
        "            and returns a transformed version.\n",
        "    Returns:\n",
        "        video (Tensor[T, H, W, C]): the `T` video frames\n",
        "        audio(Tensor[K, L]): the audio frames, where `K` is the number of channels\n",
        "            and `L` is the number of points\n",
        "        label (int): class of the video clip\n",
        "    \"\"\"\n",
        "    def __init__(self, root=\"\"):\n",
        "        super(HMDB51, self).__init__(root)\n",
        "\n",
        "    def init_data(self, root, frames_per_clip, step_between_clips=1,\n",
        "                 frame_rate=None, train=True, transform=None,\n",
        "                 _precomputed_metadata=None, num_workers=1, _video_width=0,\n",
        "                 _video_height=0, _video_min_dimension=0, _audio_samples=0):\n",
        "        super(HMDB51, self).__init__(root)\n",
        "        extensions = ('avi',)\n",
        "        if train:\n",
        "            root = root + \"/train\"\n",
        "        else:\n",
        "            root = root + \"/test\"\n",
        "        classes = sorted(list_dir(root))\n",
        "        class_to_idx = {class_: i for (i, class_) in enumerate(classes)}\n",
        "        print(class_to_idx)\n",
        "        self.samples = []\n",
        "        for target_class in sorted(class_to_idx.keys()):\n",
        "            class_index = class_to_idx[target_class]\n",
        "            target_dir = os.path.join(root, target_class)\n",
        "            for root_curr, _, fnames in sorted(os.walk(target_dir, followlinks=True)):\n",
        "                for fname in sorted(fnames):\n",
        "                    path = os.path.join(root_curr, fname)\n",
        "                    if os.path.isfile(path):\n",
        "                        item = path, class_index\n",
        "                        self.samples.append(item)\n",
        "\n",
        "        video_paths = [path for (path, _) in self.samples]\n",
        "        video_clips = VideoClips(\n",
        "            video_paths,\n",
        "            frames_per_clip,\n",
        "            step_between_clips,\n",
        "            frame_rate,\n",
        "            _precomputed_metadata,\n",
        "            num_workers=num_workers,\n",
        "            _video_width=_video_width,\n",
        "            _video_height=_video_height,\n",
        "            _video_min_dimension=_video_min_dimension,\n",
        "            _audio_samples=_audio_samples,\n",
        "        )\n",
        "        self.train = train\n",
        "        self.classes = classes\n",
        "        self.video_clips_metadata = video_clips.metadata\n",
        "        self.indices = self.get_indices(video_paths)\n",
        "        self.video_clips = video_clips.subset(self.indices)\n",
        "        self.transform = transform\n",
        "\n",
        "    @property\n",
        "    def metadata(self):\n",
        "        return self.video_clips_metadata\n",
        "\n",
        "    def get_indices(self, video_list):\n",
        "        indices = []\n",
        "        for video_index, video_path in enumerate(video_list):\n",
        "            indices.append(video_index)\n",
        "        return indices\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.video_clips.num_clips()\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        video, _, _, video_idx = self.video_clips.get_clip(idx)\n",
        "        sample_index = self.indices[video_idx]\n",
        "        _, class_index = self.samples[sample_index]\n",
        "\n",
        "        if self.transform is not None:\n",
        "            video = video.permute(0, 3, 1, 2)\n",
        "            video = self.transform(video)\n",
        "            video = video.squeeze(0)\n",
        "\n",
        "        return video, class_index, sample_index\n",
        "\n",
        "    def state_dict(self):\n",
        "        state = {\"video_clips\": self.video_clips,\n",
        "                 \"indices\": self.indices,\n",
        "                 \"samples\": self.samples,\n",
        "                 \"transform\": self.transform,\n",
        "                 \"metadata\": self.video_clips_metadata}\n",
        "\n",
        "        return state\n",
        "\n",
        "    def load_state_dict(self, state):\n",
        "        self.video_clips = state[\"video_clips\"]\n",
        "        self.indices = state[\"indices\"]\n",
        "        self.samples = state[\"samples\"]\n",
        "        self.transform = state[\"transform\"]\n",
        "        self.video_clips_metadata = state[\"metadata\"]\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "class BuildDataLoader(torch.utils.data.DataLoader):\n",
        "    def __init__(self, dataset, batch_size, shuffle, num_workers):\n",
        "        self.dataset = dataset\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.num_workers = num_workers\n",
        "        self.w_frame = 224\n",
        "        self.h_frame = 224\n",
        "\n",
        "    def collect_fn(self, batch):\n",
        "        video_list = []\n",
        "        label_list = []\n",
        "        index_list = []\n",
        "\n",
        "        for video, cl_index, s_index in batch:\n",
        "            video_list.append(video)\n",
        "            label_list.append(cl_index)\n",
        "            index_list.append(s_index)\n",
        "\n",
        "        data = {\"videos\": torch.stack(video_list),\n",
        "                \"labels\": label_list,\n",
        "                \"indexes\": index_list\n",
        "                }\n",
        "\n",
        "        return data\n",
        "\n",
        "    def loader(self):\n",
        "        return DataLoader(self.dataset,\n",
        "                          batch_size=self.batch_size,\n",
        "                          shuffle=self.shuffle,\n",
        "                          num_workers=self.num_workers,\n",
        "                          collate_fn=self.collect_fn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwpGXLdtuq8i"
      },
      "source": [
        "# Mount drive\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6nsoL9Kuqil",
        "outputId": "9f4161f7-ae2f-4549-892f-0d2c38ff29b3"
      },
      "source": [
        "import os\n",
        "import io\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount google drive\n",
        "DRIVE_MOUNT='/content/drive'\n",
        "\n",
        "drive.mount(DRIVE_MOUNT)\n",
        "\n",
        "\n",
        "# create folder to write data to\n",
        "DATA_FOLDER = os.path.join(DRIVE_MOUNT, 'Shared drives', 'CIS680 Final Project', 'data')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n88CrhloR-sz"
      },
      "source": [
        "# Get dataset state files (don't need to run)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "ae1a4bcc64044ddc9727df8e3559a48e",
            "13ba0b5b42f24229acb9af66f706c1c5",
            "24cb4cbd58654b219915f6e7a969bdb1",
            "0a9f5b247edc4a649f9b909375d0637d",
            "2bd74561b55d47f5803020766de2ba23",
            "13ceb1216a054c63a02ff0dae60a1672",
            "3e95f9140cb546e18af501f7f5f0e010",
            "253f7a683c334d668752ba73fbd55a16",
            "f213f81d791644bcb0d5a0c31229cbb0",
            "88f08d5682444fde975971fc7d5461f6",
            "fea90632c12e4a50a04f7d92ccdd1f7a",
            "d5f4fb5cfc6a4025b8a6ef67039b534d",
            "77ad60168cdf4f17b650577e2bd3808c",
            "ad143a7b6442464ab0acd97540865ddd",
            "a0d1b038c86144babdf54de42020940d",
            "ba5fe1bba25a4771b568addad75dc4e7"
          ]
        },
        "id": "uh3DK97NR9al",
        "outputId": "7c0abdca-6e94-4ffc-f360-fe25bed360cd"
      },
      "source": [
        "import torchvision.transforms as tf\n",
        "\n",
        "transform = tf.Compose([tf.Resize(256), tf.RandomCrop(224)])\n",
        "\n",
        "train_dataset = HMDB51(DATA_FOLDER)\n",
        "test_dataset = HMDB51(DATA_FOLDER)\n",
        "\n",
        "train_dataset.init_data(DATA_FOLDER, frames_per_clip=1, transform=transform)\n",
        "torch.save(train_dataset.state_dict(), DATA_FOLDER + \"/train_dataset_1.pt\")\n",
        "\n",
        "test_dataset.init_data(DATA_FOLDER, frames_per_clip=1, train=False, transform=transform)\n",
        "torch.save(test_dataset.state_dict(), DATA_FOLDER + \"/test_dataset_1.pt\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'brush_hair': 0, 'cartwheel': 1, 'catch': 2, 'chew': 3, 'clap': 4, 'climb': 5, 'climb_stairs': 6, 'dive': 7, 'draw_sword': 8, 'dribble': 9, 'drink': 10, 'eat': 11, 'fall_floor': 12, 'fencing': 13, 'flic_flac': 14, 'golf': 15, 'handstand': 16, 'hit': 17, 'hug': 18, 'jump': 19, 'kick': 20, 'kick_ball': 21, 'kiss': 22, 'laugh': 23, 'pick': 24, 'pour': 25, 'pullup': 26, 'punch': 27, 'push': 28, 'pushup': 29, 'ride_bike': 30, 'ride_horse': 31, 'run': 32, 'shake_hands': 33, 'shoot_ball': 34, 'shoot_bow': 35, 'shoot_gun': 36, 'sit': 37, 'situp': 38, 'smile': 39, 'smoke': 40, 'somersault': 41, 'stand': 42, 'swing_baseball': 43, 'sword': 44, 'sword_exercise': 45, 'talk': 46, 'throw': 47, 'turn': 48, 'walk': 49, 'wave': 50}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae1a4bcc64044ddc9727df8e3559a48e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=330.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'brush_hair': 0, 'cartwheel': 1, 'catch': 2, 'chew': 3, 'clap': 4, 'climb': 5, 'climb_stairs': 6, 'dive': 7, 'draw_sword': 8, 'dribble': 9, 'drink': 10, 'eat': 11, 'fall_floor': 12, 'fencing': 13, 'flic_flac': 14, 'golf': 15, 'handstand': 16, 'hit': 17, 'hug': 18, 'jump': 19, 'kick': 20, 'kick_ball': 21, 'kiss': 22, 'laugh': 23, 'pick': 24, 'pour': 25, 'pullup': 26, 'punch': 27, 'push': 28, 'pushup': 29, 'ride_bike': 30, 'ride_horse': 31, 'run': 32, 'shake_hands': 33, 'shoot_ball': 34, 'shoot_bow': 35, 'shoot_gun': 36, 'sit': 37, 'situp': 38, 'smile': 39, 'smoke': 40, 'somersault': 41, 'stand': 42, 'swing_baseball': 43, 'sword': 44, 'sword_exercise': 45, 'talk': 46, 'throw': 47, 'turn': 48, 'walk': 49, 'wave': 50}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f213f81d791644bcb0d5a0c31229cbb0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=97.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n37IkHXBt2sn"
      },
      "source": [
        "# Load Dataset and Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tpb1CP4et6AW",
        "outputId": "5439cd84-5774-4744-ddb6-5930dd3cccc7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "train_dataset = HMDB51(DATA_FOLDER)\n",
        "test_dataset = HMDB51(DATA_FOLDER)\n",
        "\n",
        "train_dataset.load_state_dict(torch.load(DATA_FOLDER + \"/train_dataset_1.pt\"))\n",
        "test_dataset.load_state_dict(torch.load(DATA_FOLDER + \"/test_dataset_1.pt\"))\n",
        "\n",
        "batch_size = 128\n",
        "train_build_loader = BuildDataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
        "train_loader = train_build_loader.loader()\n",
        "test_build_loader = BuildDataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)\n",
        "test_loader = test_build_loader.loader()\n",
        "\n",
        "print(\"Length of train_loader: {}\".format(len(train_loader)))\n",
        "print(\"Length of test_loader: {}\".format(len(test_loader)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of train_loader: 3851\n",
            "Length of test_loader: 1128\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo6FpL-ddpSI"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8J0AEPhdqg8"
      },
      "source": [
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torchvision import transforms\n",
        "from torch import nn, Tensor\n",
        "import random\n",
        "import torchvision\n",
        "import torchvision.models as models\n",
        "import torch.optim as optim\n",
        "\n",
        "\n",
        "class SpatialStream(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, \n",
        "                 device='cuda',\n",
        "                 num_classes=51,\n",
        "                 dropout_probability=0.5,\n",
        "                 train_resnet=True):\n",
        "\n",
        "        # Initialize the stream layers\n",
        "        super(SpatialStream, self).__init__()\n",
        "        self.device = device\n",
        "        self.num_classes = num_classes\n",
        "\n",
        "        # Spatial Pretrained Resnet-50\n",
        "        self.spatial = models.resnet50(pretrained=True)\n",
        "        for param in self.spatial.parameters():\n",
        "            param.requires_grad = train_resnet  # False: Freezes the weights of the pre-trained model\n",
        "\n",
        "        # Add FC layers to Spatial Resnet-50\n",
        "        self.spatial.fc = nn.Sequential(nn.Linear(2048, 1024),\n",
        "                                nn.ReLU(),\n",
        "                                nn.Dropout(p=dropout_probability),\n",
        "                                nn.Linear(1024, self.num_classes),\n",
        "                                nn.ReLU(),\n",
        "                                nn.Dropout(p=dropout_probability),\n",
        "                                nn.Softmax())\n",
        "\n",
        "    def forward(self, X):\n",
        "        return self.spatial(X)\n",
        "\n",
        "    def compute_loss(self, output, labels):\n",
        "        criterion = nn.CrossEntropyLoss()\n",
        "        loss = criterion(output, labels)\n",
        "        return loss"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pEvnrSVeftj"
      },
      "source": [
        "# Train & Test Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggkzqmwzefQq"
      },
      "source": [
        "  def train(epoch):\n",
        "    spatial.train()\n",
        "    counter = 0\n",
        "    train_loss = 0\n",
        "    log_interval = 100\n",
        "    save_interval = 250\n",
        "\n",
        "    epoch_loss = []\n",
        "    log_int_loss = 0\n",
        "\n",
        "    for iter, data in enumerate(train_loader, 0):\n",
        "\n",
        "        # load data\n",
        "        videos = data[\"videos\"]\n",
        "        labels = torch.tensor(data[\"labels\"])\n",
        "        indexes = data[\"indexes\"]\n",
        "\n",
        "        videos = videos.type(torch.FloatTensor)\n",
        "        videos = videos.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # get spatial output\n",
        "        output = spatial(videos)\n",
        "        output = output.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # calculate losses\n",
        "        loss = spatial.compute_loss(output, labels)\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        train_loss += loss.item()\n",
        "\n",
        "        # Logging\n",
        "        log_int_loss += loss.item()\n",
        "        epoch_loss.append(loss.item())\n",
        "\n",
        "        if counter == 0:\n",
        "            print('Epoch: ', epoch, ', Batch: ', iter, ', loss avg over log interval: ', log_int_loss)\n",
        "            train_loss_list.append(train_loss / (iter + 1) * batch_size)\n",
        "            train_counter.append((iter + 1) * batch_size + epoch * len(train_loader.dataset))\n",
        "            log_int_loss = 0\n",
        "        elif counter % log_interval == log_interval - 1:\n",
        "            print('Epoch: ', epoch, ', Batch: ', iter, ', loss avg over log interval: ', log_int_loss / log_interval)\n",
        "            train_loss_list.append(train_loss / (iter + 1) * batch_size)\n",
        "            train_counter.append((iter + 1) * batch_size + epoch * len(train_loader.dataset))\n",
        "            log_int_loss = 0\n",
        "\n",
        "        # Save every save_interval iterations\n",
        "        if counter % save_interval == save_interval - 1:\n",
        "            print('saving model')\n",
        "            save_path = os.path.join(EPOCH_SAVE_PREFIX, 'spatial_epoch' + str(epoch) + '_iter_' + str(counter))\n",
        "            torch.save({\n",
        "                'epoch': epoch,\n",
        "                'train_total_loss_list': train_loss_list,\n",
        "                'epoch_total_loss_list': epoch_loss_list,\n",
        "                'test_loss_list': test_loss_list,\n",
        "                'train_counter': train_counter,\n",
        "                'accuracy_list': accuracy_list,\n",
        "                'model_state_dict': spatial.state_dict(),\n",
        "                'optimizer_state_dict': optimizer.state_dict()\n",
        "            }, save_path)\n",
        "\n",
        "        counter += 1\n",
        "\n",
        "\n",
        "    avg_loss = sum(epoch_loss) / len(epoch_loss)\n",
        "    epoch_loss_list.append(avg_loss)\n",
        "    print('Epoch: ', epoch, ', avg total loss: ', avg_loss)\n",
        "\n",
        "def test():\n",
        "    spatial.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for iter, data in enumerate(test_loader, 0):\n",
        "\n",
        "            # load data\n",
        "            videos = data[\"videos\"]\n",
        "            labels = torch.tensor(data[\"labels\"])\n",
        "            indexes = data[\"indexes\"]\n",
        "\n",
        "            videos = videos.type(torch.FloatTensor)\n",
        "            videos = videos.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            # get spatial output\n",
        "            output = spatial(videos)\n",
        "            output = output.to(device)\n",
        "\n",
        "            # calculate losses\n",
        "            loss = spatial.compute_loss(output, labels)\n",
        "\n",
        "            test_loss += loss.item()\n",
        "\n",
        "            # calculate number of correct predictions in batch to get accuracy\n",
        "            correct += sum(torch.argmax(output,1) == labels).item()\n",
        "            if iter % 100 == 0:\n",
        "                print (\"iter  \", iter)\n",
        "                print(\"accuracy so far = \", correct / ((iter + 1) * len(labels)))\n",
        "\n",
        "    # Log\n",
        "    test_loss_list.append(test_loss / len(test_loader.dataset))\n",
        "    accuracy = correct / len(test_loader.dataset)\n",
        "    accuracy_list.append(accuracy)\n",
        "    print('Avg Validation Loss: ', test_loss / len(test_loader.dataset))\n",
        "    print('Accuracy: ', accuracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxNfXCLLYcbW"
      },
      "source": [
        "# Main Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiW7i49DYbwc",
        "outputId": "a45b4cff-4c3c-4343-b3cc-ac6a46920205"
      },
      "source": [
        "EPOCH_SAVE_PREFIX = '/content/drive/Shared drives/CIS680 Final Project/models/spatial/'\n",
        "\n",
        "# Device\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "torch.random.manual_seed(1)\n",
        "\n",
        "# model\n",
        "learning_rate = 0.001\n",
        "spatial = SpatialStream()\n",
        "spatial.to(device)\n",
        "optimizer = optim.SGD(spatial.parameters(), lr=learning_rate, momentum=0.9)\n",
        "\n",
        "# Epochs\n",
        "num_epochs = 50\n",
        "\n",
        "# Logging setup: train\n",
        "train_loss_list = []\n",
        "epoch_loss_list = []\n",
        "train_counter = []\n",
        "\n",
        "# Logging setup: test\n",
        "test_loss_list = []\n",
        "accuracy_list = []\n",
        "epoch_list = np.arange(num_epochs)\n",
        "\n",
        "# epoch loop\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "    # Train & Validate\n",
        "    train(epoch)\n",
        "    test()\n",
        "\n",
        "    # Save Model\n",
        "    save_path = os.path.join(EPOCH_SAVE_PREFIX, 'spatial_epoch' + str(epoch))\n",
        "    torch.save({\n",
        "        'epoch': epoch,\n",
        "        'train_total_loss_list': train_loss_list,\n",
        "        'epoch_total_loss_list': epoch_loss_list,\n",
        "        'test_loss_list': test_loss_list,\n",
        "        'train_counter': train_counter,\n",
        "        'accuracy_list': accuracy_list,\n",
        "        'model_state_dict': spatial.state_dict(),\n",
        "        'optimizer_state_dict': optimizer.state_dict()\n",
        "    }, save_path)\n",
        "\n",
        "    print(\"Epoch %d/%d Completed\" % (epoch, num_epochs - 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/io/video.py:117: UserWarning: The pts_unit 'pts' gives wrong results and will be removed in a follow-up version. Please use pts_unit 'sec'.\n",
            "  + \"follow-up version. Please use pts_unit 'sec'.\"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  0 , Batch:  0 , loss avg over log interval:  3.9320425987243652\n",
            "Epoch:  0 , Batch:  99 , loss avg over log interval:  3.8924271631240845\n",
            "Epoch:  0 , Batch:  199 , loss avg over log interval:  3.9314334273338316\n",
            "saving model\n",
            "Epoch:  0 , Batch:  299 , loss avg over log interval:  3.930895450115204\n",
            "Epoch:  0 , Batch:  399 , loss avg over log interval:  3.929605519771576\n",
            "Epoch:  0 , Batch:  499 , loss avg over log interval:  3.9242276430130003\n",
            "saving model\n",
            "Epoch:  0 , Batch:  599 , loss avg over log interval:  3.906380925178528\n",
            "Epoch:  0 , Batch:  699 , loss avg over log interval:  3.895463914871216\n",
            "saving model\n",
            "Epoch:  0 , Batch:  799 , loss avg over log interval:  3.9005392670631407\n",
            "Epoch:  0 , Batch:  899 , loss avg over log interval:  3.896544728279114\n",
            "Epoch:  0 , Batch:  999 , loss avg over log interval:  3.890415885448456\n",
            "saving model\n",
            "Epoch:  0 , Batch:  1099 , loss avg over log interval:  3.8922301173210143\n",
            "Epoch:  0 , Batch:  1199 , loss avg over log interval:  3.8916511154174804\n",
            "saving model\n",
            "Epoch:  0 , Batch:  1299 , loss avg over log interval:  3.8920888447761537\n",
            "Epoch:  0 , Batch:  1399 , loss avg over log interval:  3.889365351200104\n",
            "Epoch:  0 , Batch:  1499 , loss avg over log interval:  3.8816887640953066\n",
            "saving model\n",
            "Epoch:  0 , Batch:  1599 , loss avg over log interval:  3.8815790843963622\n",
            "Epoch:  0 , Batch:  1699 , loss avg over log interval:  3.8780367231369017\n",
            "saving model\n",
            "Epoch:  0 , Batch:  1799 , loss avg over log interval:  3.8749545884132384\n",
            "Epoch:  0 , Batch:  1899 , loss avg over log interval:  3.873163192272186\n",
            "Epoch:  0 , Batch:  1999 , loss avg over log interval:  3.8695044207572935\n",
            "saving model\n",
            "Epoch:  0 , Batch:  2099 , loss avg over log interval:  3.872304530143738\n",
            "Epoch:  0 , Batch:  2199 , loss avg over log interval:  3.869849891662598\n",
            "saving model\n",
            "Epoch:  0 , Batch:  2299 , loss avg over log interval:  3.8623984026908875\n",
            "Epoch:  0 , Batch:  2399 , loss avg over log interval:  3.862843735218048\n",
            "Epoch:  0 , Batch:  2499 , loss avg over log interval:  3.866309244632721\n",
            "saving model\n",
            "Epoch:  0 , Batch:  2599 , loss avg over log interval:  3.863068187236786\n",
            "Epoch:  0 , Batch:  2699 , loss avg over log interval:  3.8627669405937195\n",
            "saving model\n",
            "Epoch:  0 , Batch:  2799 , loss avg over log interval:  3.85834614276886\n",
            "Epoch:  0 , Batch:  2899 , loss avg over log interval:  3.856306045055389\n",
            "Epoch:  0 , Batch:  2999 , loss avg over log interval:  3.8538205289840697\n",
            "saving model\n",
            "Epoch:  0 , Batch:  3099 , loss avg over log interval:  3.850872142314911\n",
            "Epoch:  0 , Batch:  3199 , loss avg over log interval:  3.847743933200836\n",
            "saving model\n",
            "Epoch:  0 , Batch:  3299 , loss avg over log interval:  3.846995406150818\n",
            "Epoch:  0 , Batch:  3399 , loss avg over log interval:  3.84543488740921\n",
            "Epoch:  0 , Batch:  3499 , loss avg over log interval:  3.842412986755371\n",
            "saving model\n",
            "Epoch:  0 , Batch:  3599 , loss avg over log interval:  3.8426721715927123\n",
            "Epoch:  0 , Batch:  3699 , loss avg over log interval:  3.839512836933136\n",
            "saving model\n",
            "Epoch:  0 , Batch:  3799 , loss avg over log interval:  3.842078142166138\n",
            "Epoch:  0 , avg total loss:  3.8770243316710817\n",
            "iter   0\n",
            "accuracy so far =  0.9921875\n",
            "iter   100\n",
            "accuracy so far =  0.5314047029702971\n",
            "iter   200\n",
            "accuracy so far =  0.41697761194029853\n",
            "iter   300\n",
            "accuracy so far =  0.27849875415282394\n",
            "iter   400\n",
            "accuracy so far =  0.20904769326683292\n",
            "iter   500\n",
            "accuracy so far =  0.16732160678642716\n",
            "iter   600\n",
            "accuracy so far =  0.20096713810316139\n",
            "iter   700\n",
            "accuracy so far =  0.17229850213980027\n",
            "iter   800\n",
            "accuracy so far =  0.18265254369538078\n",
            "iter   900\n",
            "accuracy so far =  0.17138075749167592\n",
            "iter   1000\n",
            "accuracy so far =  0.1542598026973027\n",
            "iter   1100\n",
            "accuracy so far =  0.1556326634877384\n",
            "Avg Validation Loss:  0.030225420123708917\n",
            "Accuracy:  0.15841618457061696\n",
            "Epoch 0/49 Completed\n",
            "Epoch:  1 , Batch:  0 , loss avg over log interval:  3.8774733543395996\n",
            "Epoch:  1 , Batch:  99 , loss avg over log interval:  3.796684684753418\n",
            "Epoch:  1 , Batch:  199 , loss avg over log interval:  3.832868616580963\n",
            "saving model\n",
            "Epoch:  1 , Batch:  299 , loss avg over log interval:  3.838472936153412\n",
            "Epoch:  1 , Batch:  399 , loss avg over log interval:  3.832034330368042\n",
            "Epoch:  1 , Batch:  499 , loss avg over log interval:  3.8334626364707947\n",
            "saving model\n",
            "Epoch:  1 , Batch:  599 , loss avg over log interval:  3.8289686107635497\n",
            "Epoch:  1 , Batch:  699 , loss avg over log interval:  3.831216480731964\n",
            "saving model\n",
            "Epoch:  1 , Batch:  799 , loss avg over log interval:  3.821666667461395\n",
            "Epoch:  1 , Batch:  899 , loss avg over log interval:  3.8228848958015442\n",
            "Epoch:  1 , Batch:  999 , loss avg over log interval:  3.813214166164398\n",
            "saving model\n",
            "Epoch:  1 , Batch:  1099 , loss avg over log interval:  3.821969428062439\n",
            "Epoch:  1 , Batch:  1199 , loss avg over log interval:  3.814561471939087\n",
            "saving model\n",
            "Epoch:  1 , Batch:  1299 , loss avg over log interval:  3.815798282623291\n",
            "Epoch:  1 , Batch:  1399 , loss avg over log interval:  3.8148784947395327\n",
            "Epoch:  1 , Batch:  1499 , loss avg over log interval:  3.813563497066498\n",
            "saving model\n",
            "Epoch:  1 , Batch:  1599 , loss avg over log interval:  3.8085448694229127\n",
            "Epoch:  1 , Batch:  1699 , loss avg over log interval:  3.8064933753013612\n",
            "saving model\n",
            "Epoch:  1 , Batch:  1799 , loss avg over log interval:  3.8027115058898926\n",
            "Epoch:  1 , Batch:  1899 , loss avg over log interval:  3.8010514616966247\n",
            "Epoch:  1 , Batch:  1999 , loss avg over log interval:  3.803515954017639\n",
            "saving model\n",
            "Epoch:  1 , Batch:  2099 , loss avg over log interval:  3.800175180435181\n",
            "Epoch:  1 , Batch:  2199 , loss avg over log interval:  3.8000399565696714\n",
            "saving model\n",
            "Epoch:  1 , Batch:  2299 , loss avg over log interval:  3.7920373702049255\n",
            "Epoch:  1 , Batch:  2399 , loss avg over log interval:  3.7897414803504943\n",
            "Epoch:  1 , Batch:  2499 , loss avg over log interval:  3.785543360710144\n",
            "saving model\n",
            "Epoch:  1 , Batch:  2599 , loss avg over log interval:  3.7882925963401792\n",
            "Epoch:  1 , Batch:  2699 , loss avg over log interval:  3.782355561256409\n",
            "saving model\n",
            "Epoch:  1 , Batch:  2799 , loss avg over log interval:  3.788655743598938\n",
            "Epoch:  1 , Batch:  2899 , loss avg over log interval:  3.779375650882721\n",
            "Epoch:  1 , Batch:  2999 , loss avg over log interval:  3.781195945739746\n",
            "saving model\n",
            "Epoch:  1 , Batch:  3099 , loss avg over log interval:  3.7837581133842466\n",
            "Epoch:  1 , Batch:  3199 , loss avg over log interval:  3.7817740345001223\n",
            "saving model\n",
            "Epoch:  1 , Batch:  3299 , loss avg over log interval:  3.769518084526062\n",
            "Epoch:  1 , Batch:  3399 , loss avg over log interval:  3.7689779925346376\n",
            "Epoch:  1 , Batch:  3499 , loss avg over log interval:  3.7720657110214235\n",
            "saving model\n",
            "Epoch:  1 , Batch:  3599 , loss avg over log interval:  3.7734732484817504\n",
            "Epoch:  1 , Batch:  3699 , loss avg over log interval:  3.7652554297447205\n",
            "saving model\n",
            "Epoch:  1 , Batch:  3799 , loss avg over log interval:  3.7650459098815916\n",
            "Epoch:  1 , avg total loss:  3.8011713642728573\n",
            "iter   0\n",
            "accuracy so far =  0.984375\n",
            "iter   100\n",
            "accuracy so far =  0.4573019801980198\n",
            "iter   200\n",
            "accuracy so far =  0.349696828358209\n",
            "iter   300\n",
            "accuracy so far =  0.30907392026578073\n",
            "iter   400\n",
            "accuracy so far =  0.23199812967581046\n",
            "iter   500\n",
            "accuracy so far =  0.21237213073852296\n",
            "iter   600\n",
            "accuracy so far =  0.2778702163061564\n",
            "iter   700\n",
            "accuracy so far =  0.25166057417974325\n",
            "iter   800\n",
            "accuracy so far =  0.29011587078651685\n",
            "iter   900\n",
            "accuracy so far =  0.26712506936736957\n",
            "iter   1000\n",
            "accuracy so far =  0.25256774475524474\n",
            "iter   1100\n",
            "accuracy so far =  0.26896003633060855\n",
            "Avg Validation Loss:  0.02966945047385478\n",
            "Accuracy:  0.26872207018394706\n",
            "Epoch 1/49 Completed\n",
            "Epoch:  2 , Batch:  0 , loss avg over log interval:  3.7785825729370117\n",
            "Epoch:  2 , Batch:  99 , loss avg over log interval:  3.725133068561554\n",
            "Epoch:  2 , Batch:  199 , loss avg over log interval:  3.7594811058044435\n",
            "saving model\n",
            "Epoch:  2 , Batch:  299 , loss avg over log interval:  3.7586860036849976\n",
            "Epoch:  2 , Batch:  399 , loss avg over log interval:  3.75706248998642\n",
            "Epoch:  2 , Batch:  499 , loss avg over log interval:  3.7535384821891786\n",
            "saving model\n",
            "Epoch:  2 , Batch:  599 , loss avg over log interval:  3.7581454014778135\n",
            "Epoch:  2 , Batch:  699 , loss avg over log interval:  3.752154595851898\n",
            "saving model\n",
            "Epoch:  2 , Batch:  799 , loss avg over log interval:  3.7530227303504944\n",
            "Epoch:  2 , Batch:  899 , loss avg over log interval:  3.748232154846191\n",
            "Epoch:  2 , Batch:  999 , loss avg over log interval:  3.7487113857269287\n",
            "saving model\n",
            "Epoch:  2 , Batch:  1099 , loss avg over log interval:  3.7460082030296324\n",
            "Epoch:  2 , Batch:  1199 , loss avg over log interval:  3.752404646873474\n",
            "saving model\n",
            "Epoch:  2 , Batch:  1299 , loss avg over log interval:  3.747168643474579\n",
            "Epoch:  2 , Batch:  1399 , loss avg over log interval:  3.7438337206840515\n",
            "Epoch:  2 , Batch:  1499 , loss avg over log interval:  3.7459705114364623\n",
            "saving model\n",
            "Epoch:  2 , Batch:  1599 , loss avg over log interval:  3.747836310863495\n",
            "Epoch:  2 , Batch:  1699 , loss avg over log interval:  3.740805649757385\n",
            "saving model\n",
            "Epoch:  2 , Batch:  1799 , loss avg over log interval:  3.742447452545166\n",
            "Epoch:  2 , Batch:  1899 , loss avg over log interval:  3.739665780067444\n",
            "Epoch:  2 , Batch:  1999 , loss avg over log interval:  3.7366249179840088\n",
            "saving model\n",
            "Epoch:  2 , Batch:  2099 , loss avg over log interval:  3.738314824104309\n",
            "Epoch:  2 , Batch:  2199 , loss avg over log interval:  3.7396478199958803\n",
            "saving model\n",
            "Epoch:  2 , Batch:  2299 , loss avg over log interval:  3.7350535655021666\n",
            "Epoch:  2 , Batch:  2399 , loss avg over log interval:  3.7348633337020876\n",
            "Epoch:  2 , Batch:  2499 , loss avg over log interval:  3.731687970161438\n",
            "saving model\n",
            "Epoch:  2 , Batch:  2599 , loss avg over log interval:  3.738045530319214\n",
            "Epoch:  2 , Batch:  2699 , loss avg over log interval:  3.730348918437958\n",
            "saving model\n",
            "Epoch:  2 , Batch:  2799 , loss avg over log interval:  3.7267900609970095\n",
            "Epoch:  2 , Batch:  2899 , loss avg over log interval:  3.7288767504692077\n",
            "Epoch:  2 , Batch:  2999 , loss avg over log interval:  3.73171160697937\n",
            "saving model\n",
            "Epoch:  2 , Batch:  3099 , loss avg over log interval:  3.7238827085494997\n",
            "Epoch:  2 , Batch:  3199 , loss avg over log interval:  3.7259604859352113\n",
            "saving model\n",
            "Epoch:  2 , Batch:  3299 , loss avg over log interval:  3.7268344163894653\n",
            "Epoch:  2 , Batch:  3399 , loss avg over log interval:  3.7290000700950623\n",
            "Epoch:  2 , Batch:  3499 , loss avg over log interval:  3.718089165687561\n",
            "saving model\n",
            "Epoch:  2 , Batch:  3599 , loss avg over log interval:  3.726611156463623\n",
            "Epoch:  2 , Batch:  3699 , loss avg over log interval:  3.7168417716026307\n",
            "saving model\n",
            "Epoch:  2 , Batch:  3799 , loss avg over log interval:  3.712840619087219\n",
            "Epoch:  2 , avg total loss:  3.7394093579361884\n",
            "iter   0\n",
            "accuracy so far =  0.9921875\n",
            "iter   100\n",
            "accuracy so far =  0.5333384900990099\n",
            "iter   200\n",
            "accuracy so far =  0.4566231343283582\n",
            "iter   300\n",
            "accuracy so far =  0.3784001245847176\n",
            "iter   400\n",
            "accuracy so far =  0.28403600374064836\n",
            "iter   500\n",
            "accuracy so far =  0.2525885728542914\n",
            "iter   600\n",
            "accuracy so far =  0.29831790765391014\n",
            "iter   700\n",
            "accuracy so far =  0.2784972360912982\n",
            "iter   800\n",
            "accuracy so far =  0.3128608770287141\n",
            "iter   900\n",
            "accuracy so far =  0.3127167730299667\n",
            "iter   1000\n",
            "accuracy so far =  0.3227631743256743\n",
            "iter   1100\n",
            "accuracy so far =  0.34442410308810173\n",
            "Avg Validation Loss:  0.029190267289056923\n",
            "Accuracy:  0.3424186787681436\n",
            "Epoch 2/49 Completed\n",
            "Epoch:  3 , Batch:  0 , loss avg over log interval:  3.724855899810791\n",
            "Epoch:  3 , Batch:  99 , loss avg over log interval:  3.688034884929657\n",
            "Epoch:  3 , Batch:  199 , loss avg over log interval:  3.720089120864868\n",
            "saving model\n",
            "Epoch:  3 , Batch:  299 , loss avg over log interval:  3.7135028409957886\n",
            "Epoch:  3 , Batch:  399 , loss avg over log interval:  3.7154658007621766\n",
            "Epoch:  3 , Batch:  499 , loss avg over log interval:  3.716395609378815\n",
            "saving model\n",
            "Epoch:  3 , Batch:  599 , loss avg over log interval:  3.718493938446045\n",
            "Epoch:  3 , Batch:  699 , loss avg over log interval:  3.710838577747345\n",
            "saving model\n",
            "Epoch:  3 , Batch:  799 , loss avg over log interval:  3.7159865522384643\n",
            "Epoch:  3 , Batch:  899 , loss avg over log interval:  3.7179988384246827\n",
            "Epoch:  3 , Batch:  999 , loss avg over log interval:  3.710153789520264\n",
            "saving model\n",
            "Epoch:  3 , Batch:  1099 , loss avg over log interval:  3.716684093475342\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDmKw9pMH3NJ"
      },
      "source": [
        "# Resume Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "e2ac1ce20b5e41208342e5a917726d4e",
            "558faa8cc4124de49c3a432e37c04007",
            "604efc3f04374c5fb6d42b7c7168a035",
            "0c11d17f29ab4864845384c6862f629f",
            "e3811e599c794585aded5b04075a8109",
            "790b18b1f19a494bb6bc15cfb14f4e39",
            "425029eadae5419b94cda4c64d988d75",
            "4767defab70242f799e31ec3d7d71b41"
          ]
        },
        "id": "0UlgNih0H2DD",
        "outputId": "db37fc16-1ac4-452b-ac3b-8451daa64af6"
      },
      "source": [
        "EPOCH_SAVE_PREFIX = '/content/drive/Shared drives/CIS680 Final Project/models/spatial/'\n",
        "\n",
        "# Device\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "torch.random.manual_seed(1)\n",
        "\n",
        "# model\n",
        "learning_rate = 0.001\n",
        "spatial = SpatialStream()\n",
        "spatial.to(device)\n",
        "optimizer = optim.SGD(spatial.parameters(), lr=learning_rate, momentum=0.9)\n",
        "\n",
        "# Epochs\n",
        "num_epochs = 50\n",
        "epoch_list = np.arange(num_epochs)\n",
        "\n",
        "\n",
        "# Load Spatial Network\n",
        "network_path = '/content/drive/Shared drives/CIS680 Final Project/models/spatial/spatial_epoch8'\n",
        "checkpoint = torch.load(network_path)\n",
        "spatial.load_state_dict(checkpoint['model_state_dict'])\n",
        "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "last_epoch = checkpoint['epoch']\n",
        "\n",
        "# Logging setup\n",
        "train_loss_list = checkpoint['train_total_loss_list']\n",
        "epoch_loss_list = checkpoint['epoch_total_loss_list']\n",
        "test_loss_list = checkpoint['test_loss_list']\n",
        "accuracy_list = checkpoint['accuracy_list']\n",
        "train_counter = checkpoint['train_counter']\n",
        "\n",
        "\n",
        "# epoch loop\n",
        "for epoch in range(last_epoch + 1, num_epochs):\n",
        "\n",
        "    # Train & Validate\n",
        "    train(epoch)\n",
        "    test()\n",
        "\n",
        "    # Save Model Version\n",
        "    save_path = os.path.join(EPOCH_SAVE_PREFIX, 'spatial_epoch' + str(epoch))\n",
        "    torch.save({\n",
        "        'epoch': epoch,\n",
        "        'train_total_loss_list': train_loss_list,\n",
        "        'epoch_total_loss_list': epoch_loss_list,\n",
        "        'test_loss_list': test_loss_list,\n",
        "        'train_counter': train_counter,\n",
        "        'accuracy_list': accuracy_list,\n",
        "        'model_state_dict': spatial.state_dict(),\n",
        "        'optimizer_state_dict': optimizer.state_dict()\n",
        "    }, save_path)\n",
        "\n",
        "    print(\"Epoch %d/%d Completed\" % (epoch, num_epochs - 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2ac1ce20b5e41208342e5a917726d4e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=102502400.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/io/video.py:117: UserWarning: The pts_unit 'pts' gives wrong results and will be removed in a follow-up version. Please use pts_unit 'sec'.\n",
            "  + \"follow-up version. Please use pts_unit 'sec'.\"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  9 , Batch:  0 , loss avg over log interval:  3.579054117202759\n",
            "Epoch:  9 , Batch:  99 , loss avg over log interval:  3.5460293793678286\n",
            "Epoch:  9 , Batch:  199 , loss avg over log interval:  3.5823871088027954\n",
            "saving model\n",
            "Epoch:  9 , Batch:  299 , loss avg over log interval:  3.5742266011238097\n",
            "Epoch:  9 , Batch:  399 , loss avg over log interval:  3.578859972953796\n",
            "Epoch:  9 , Batch:  499 , loss avg over log interval:  3.5777490186691283\n",
            "saving model\n",
            "Epoch:  9 , Batch:  599 , loss avg over log interval:  3.579861807823181\n",
            "Epoch:  9 , Batch:  699 , loss avg over log interval:  3.5729906296730043\n",
            "saving model\n",
            "Epoch:  9 , Batch:  799 , loss avg over log interval:  3.57385466337204\n",
            "Epoch:  9 , Batch:  899 , loss avg over log interval:  3.5813414573669435\n",
            "Epoch:  9 , Batch:  999 , loss avg over log interval:  3.576751308441162\n",
            "saving model\n",
            "Epoch:  9 , Batch:  1099 , loss avg over log interval:  3.5745735216140746\n",
            "Epoch:  9 , Batch:  1199 , loss avg over log interval:  3.5685772466659547\n",
            "saving model\n",
            "Epoch:  9 , Batch:  1299 , loss avg over log interval:  3.577754440307617\n",
            "Epoch:  9 , Batch:  1399 , loss avg over log interval:  3.5721950912475586\n",
            "Epoch:  9 , Batch:  1499 , loss avg over log interval:  3.5717737030982972\n",
            "saving model\n",
            "Epoch:  9 , Batch:  1599 , loss avg over log interval:  3.578251359462738\n",
            "Epoch:  9 , Batch:  1699 , loss avg over log interval:  3.5745250725746156\n",
            "saving model\n",
            "Epoch:  9 , Batch:  1799 , loss avg over log interval:  3.5757372522354127\n",
            "Epoch:  9 , Batch:  1899 , loss avg over log interval:  3.567928261756897\n",
            "Epoch:  9 , Batch:  1999 , loss avg over log interval:  3.584934277534485\n",
            "saving model\n",
            "Epoch:  9 , Batch:  2099 , loss avg over log interval:  3.577485022544861\n",
            "Epoch:  9 , Batch:  2199 , loss avg over log interval:  3.5743262720108033\n",
            "saving model\n",
            "Epoch:  9 , Batch:  2299 , loss avg over log interval:  3.5728429841995237\n",
            "Epoch:  9 , Batch:  2399 , loss avg over log interval:  3.576960034370422\n",
            "Epoch:  9 , Batch:  2499 , loss avg over log interval:  3.5769550704956057\n",
            "saving model\n",
            "Epoch:  9 , Batch:  2599 , loss avg over log interval:  3.571277866363525\n",
            "Epoch:  9 , Batch:  2699 , loss avg over log interval:  3.5720484018325807\n",
            "saving model\n",
            "Epoch:  9 , Batch:  2799 , loss avg over log interval:  3.5723149943351746\n",
            "Epoch:  9 , Batch:  2899 , loss avg over log interval:  3.5673414778709414\n",
            "Epoch:  9 , Batch:  2999 , loss avg over log interval:  3.5718661737442017\n",
            "saving model\n",
            "Epoch:  9 , Batch:  3099 , loss avg over log interval:  3.5671669363975527\n",
            "Epoch:  9 , Batch:  3199 , loss avg over log interval:  3.5618692779541017\n",
            "saving model\n",
            "Epoch:  9 , Batch:  3299 , loss avg over log interval:  3.5713111758232117\n",
            "Epoch:  9 , Batch:  3399 , loss avg over log interval:  3.575955722332001\n",
            "Epoch:  9 , Batch:  3499 , loss avg over log interval:  3.5696138167381286\n",
            "saving model\n",
            "Epoch:  9 , Batch:  3599 , loss avg over log interval:  3.569198315143585\n",
            "Epoch:  9 , Batch:  3699 , loss avg over log interval:  3.5705218291282654\n",
            "saving model\n",
            "Epoch:  9 , Batch:  3799 , loss avg over log interval:  3.5734462451934816\n",
            "Epoch:  9 , avg total loss:  3.5741825942032306\n",
            "iter   0\n",
            "accuracy so far =  0.9765625\n",
            "iter   100\n",
            "accuracy so far =  0.5340346534653465\n",
            "iter   200\n",
            "accuracy so far =  0.5257307213930348\n",
            "iter   300\n",
            "accuracy so far =  0.5087728405315615\n",
            "iter   400\n",
            "accuracy so far =  0.45086502493765584\n",
            "iter   500\n",
            "accuracy so far =  0.39622317864271456\n",
            "iter   600\n",
            "accuracy so far =  0.43774698419301167\n",
            "iter   700\n",
            "accuracy so far =  0.48912268188302427\n",
            "iter   800\n",
            "accuracy so far =  0.5168734394506866\n",
            "iter   900\n",
            "accuracy so far =  0.5080119311875694\n",
            "iter   1000\n",
            "accuracy so far =  0.503894542957043\n",
            "iter   1100\n",
            "accuracy so far =  0.5036401566757494\n",
            "Avg Validation Loss:  0.028279996895338323\n",
            "Accuracy:  0.49671250909342846\n",
            "Epoch 9/49 Completed\n",
            "Epoch:  10 , Batch:  0 , loss avg over log interval:  3.6594810485839844\n",
            "Epoch:  10 , Batch:  99 , loss avg over log interval:  3.534887034893036\n",
            "Epoch:  10 , Batch:  199 , loss avg over log interval:  3.560631122589111\n",
            "saving model\n",
            "Epoch:  10 , Batch:  299 , loss avg over log interval:  3.5786735010147095\n",
            "Epoch:  10 , Batch:  399 , loss avg over log interval:  3.570725555419922\n",
            "Epoch:  10 , Batch:  499 , loss avg over log interval:  3.5668704223632814\n",
            "saving model\n",
            "Epoch:  10 , Batch:  599 , loss avg over log interval:  3.563460876941681\n",
            "Epoch:  10 , Batch:  699 , loss avg over log interval:  3.5732044100761415\n",
            "saving model\n",
            "Epoch:  10 , Batch:  799 , loss avg over log interval:  3.564046459197998\n",
            "Epoch:  10 , Batch:  899 , loss avg over log interval:  3.559336869716644\n",
            "Epoch:  10 , Batch:  999 , loss avg over log interval:  3.561542465686798\n",
            "saving model\n",
            "Epoch:  10 , Batch:  1099 , loss avg over log interval:  3.572560291290283\n",
            "Epoch:  10 , Batch:  1199 , loss avg over log interval:  3.5655527210235594\n",
            "saving model\n",
            "Epoch:  10 , Batch:  1299 , loss avg over log interval:  3.5702772068977358\n",
            "Epoch:  10 , Batch:  1399 , loss avg over log interval:  3.563330955505371\n",
            "Epoch:  10 , Batch:  1499 , loss avg over log interval:  3.570376570224762\n",
            "saving model\n",
            "Epoch:  10 , Batch:  1599 , loss avg over log interval:  3.5713502097129823\n",
            "Epoch:  10 , Batch:  1699 , loss avg over log interval:  3.567695467472076\n",
            "saving model\n",
            "Epoch:  10 , Batch:  1799 , loss avg over log interval:  3.5666957116127014\n",
            "Epoch:  10 , Batch:  1899 , loss avg over log interval:  3.5684423208236695\n",
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            "saving model\n",
            "Epoch:  10 , Batch:  2099 , loss avg over log interval:  3.5645128321647643\n",
            "Epoch:  10 , Batch:  2199 , loss avg over log interval:  3.5729643368721007\n",
            "saving model\n",
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            "Epoch:  10 , Batch:  2399 , loss avg over log interval:  3.567446722984314\n",
            "Epoch:  10 , Batch:  2499 , loss avg over log interval:  3.5595063948631287\n",
            "saving model\n",
            "Epoch:  10 , Batch:  2599 , loss avg over log interval:  3.564383125305176\n",
            "Epoch:  10 , Batch:  2699 , loss avg over log interval:  3.559916534423828\n",
            "saving model\n",
            "Epoch:  10 , Batch:  2799 , loss avg over log interval:  3.5679066967964173\n",
            "Epoch:  10 , Batch:  2899 , loss avg over log interval:  3.5615393853187562\n",
            "Epoch:  10 , Batch:  2999 , loss avg over log interval:  3.56071852684021\n",
            "saving model\n",
            "Epoch:  10 , Batch:  3099 , loss avg over log interval:  3.568175437450409\n",
            "Epoch:  10 , Batch:  3199 , loss avg over log interval:  3.5698301005363464\n",
            "saving model\n",
            "Epoch:  10 , Batch:  3299 , loss avg over log interval:  3.556127848625183\n",
            "Epoch:  10 , Batch:  3399 , loss avg over log interval:  3.5617884612083435\n",
            "Epoch:  10 , Batch:  3499 , loss avg over log interval:  3.559564802646637\n",
            "saving model\n",
            "Epoch:  10 , Batch:  3599 , loss avg over log interval:  3.5699443101882933\n",
            "Epoch:  10 , Batch:  3699 , loss avg over log interval:  3.558623332977295\n",
            "saving model\n",
            "Epoch:  10 , Batch:  3799 , loss avg over log interval:  3.5667728781700134\n",
            "Epoch:  10 , avg total loss:  3.565968542772589\n",
            "iter   0\n",
            "accuracy so far =  0.890625\n",
            "iter   100\n",
            "accuracy so far =  0.5010829207920792\n",
            "iter   200\n",
            "accuracy so far =  0.5099113805970149\n",
            "iter   300\n",
            "accuracy so far =  0.506047549833887\n",
            "iter   400\n",
            "accuracy so far =  0.4458969763092269\n",
            "iter   500\n",
            "accuracy so far =  0.3964726796407186\n",
            "iter   600\n",
            "accuracy so far =  0.43626507903494177\n",
            "iter   700\n",
            "accuracy so far =  0.49089470399429386\n",
            "iter   800\n",
            "accuracy so far =  0.5203359082397003\n",
            "iter   900\n",
            "accuracy so far =  0.5162666481687015\n",
            "iter   1000\n",
            "accuracy so far =  0.512268981018981\n",
            "iter   1100\n",
            "accuracy so far =  0.5127370004541326\n",
            "Avg Validation Loss:  0.02823190674715891\n",
            "Accuracy:  0.5054075588041709\n",
            "Epoch 10/49 Completed\n",
            "Epoch:  11 , Batch:  0 , loss avg over log interval:  3.545201539993286\n",
            "Epoch:  11 , Batch:  99 , loss avg over log interval:  3.5204057192802427\n",
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            "saving model\n",
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            "saving model\n",
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            "saving model\n",
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            "Epoch:  11 , Batch:  899 , loss avg over log interval:  3.56130811214447\n",
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            "saving model\n",
            "Epoch:  11 , Batch:  1099 , loss avg over log interval:  3.55528270483017\n",
            "Epoch:  11 , Batch:  1199 , loss avg over log interval:  3.567438428401947\n",
            "saving model\n",
            "Epoch:  11 , Batch:  1299 , loss avg over log interval:  3.5572629570961\n",
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            "saving model\n",
            "Epoch:  11 , Batch:  1599 , loss avg over log interval:  3.563854157924652\n",
            "Epoch:  11 , Batch:  1699 , loss avg over log interval:  3.5550047087669374\n",
            "saving model\n",
            "Epoch:  11 , Batch:  1799 , loss avg over log interval:  3.5547358536720277\n",
            "Epoch:  11 , Batch:  1899 , loss avg over log interval:  3.5626550936698913\n",
            "Epoch:  11 , Batch:  1999 , loss avg over log interval:  3.555945646762848\n",
            "saving model\n",
            "Epoch:  11 , Batch:  2099 , loss avg over log interval:  3.5584344935417174\n",
            "Epoch:  11 , Batch:  2199 , loss avg over log interval:  3.556195936203003\n",
            "saving model\n",
            "Epoch:  11 , Batch:  2299 , loss avg over log interval:  3.5598151588439944\n",
            "Epoch:  11 , Batch:  2399 , loss avg over log interval:  3.5551564145088195\n",
            "Epoch:  11 , Batch:  2499 , loss avg over log interval:  3.5538482809066774\n",
            "saving model\n",
            "Epoch:  11 , Batch:  2599 , loss avg over log interval:  3.5663298058509825\n",
            "Epoch:  11 , Batch:  2699 , loss avg over log interval:  3.5543218898773192\n",
            "saving model\n",
            "Epoch:  11 , Batch:  2799 , loss avg over log interval:  3.5483792662620544\n",
            "Epoch:  11 , Batch:  2899 , loss avg over log interval:  3.5596454095840455\n",
            "Epoch:  11 , Batch:  2999 , loss avg over log interval:  3.5616452145576476\n",
            "saving model\n",
            "Epoch:  11 , Batch:  3099 , loss avg over log interval:  3.553210859298706\n",
            "Epoch:  11 , Batch:  3199 , loss avg over log interval:  3.5537309050559998\n",
            "saving model\n",
            "Epoch:  11 , Batch:  3299 , loss avg over log interval:  3.556489462852478\n",
            "Epoch:  11 , Batch:  3399 , loss avg over log interval:  3.5549196076393126\n",
            "Epoch:  11 , Batch:  3499 , loss avg over log interval:  3.5470906352996825\n",
            "saving model\n",
            "Epoch:  11 , Batch:  3599 , loss avg over log interval:  3.5557191348075867\n",
            "Epoch:  11 , Batch:  3699 , loss avg over log interval:  3.5440936303138733\n",
            "saving model\n",
            "Epoch:  11 , Batch:  3799 , loss avg over log interval:  3.546159794330597\n",
            "Epoch:  11 , avg total loss:  3.556547733444079\n",
            "iter   0\n",
            "accuracy so far =  1.0\n",
            "iter   100\n",
            "accuracy so far =  0.5326423267326733\n",
            "iter   200\n",
            "accuracy so far =  0.5050528606965174\n",
            "iter   300\n",
            "accuracy so far =  0.5082018272425249\n",
            "iter   400\n",
            "accuracy so far =  0.4494427992518703\n",
            "iter   500\n",
            "accuracy so far =  0.39458582834331335\n",
            "iter   600\n",
            "accuracy so far =  0.42543677204658903\n",
            "iter   700\n",
            "accuracy so far =  0.4794155670470756\n",
            "iter   800\n",
            "accuracy so far =  0.505500936329588\n",
            "iter   900\n",
            "accuracy so far =  0.5127635960044395\n",
            "iter   1000\n",
            "accuracy so far =  0.5056037712287712\n",
            "iter   1100\n",
            "accuracy so far =  0.5030512034514079\n",
            "Avg Validation Loss:  0.028270143950018953\n",
            "Accuracy:  0.496428447708456\n",
            "Epoch 11/49 Completed\n",
            "Epoch:  12 , Batch:  0 , loss avg over log interval:  3.593905448913574\n",
            "Epoch:  12 , Batch:  99 , loss avg over log interval:  3.521117215156555\n",
            "Epoch:  12 , Batch:  199 , loss avg over log interval:  3.552168846130371\n",
            "saving model\n",
            "Epoch:  12 , Batch:  299 , loss avg over log interval:  3.5547372937202453\n",
            "Epoch:  12 , Batch:  399 , loss avg over log interval:  3.550812337398529\n",
            "Epoch:  12 , Batch:  499 , loss avg over log interval:  3.5568406987190246\n",
            "saving model\n",
            "Epoch:  12 , Batch:  599 , loss avg over log interval:  3.5536263036727904\n",
            "Epoch:  12 , Batch:  699 , loss avg over log interval:  3.548257262706757\n",
            "saving model\n",
            "Epoch:  12 , Batch:  799 , loss avg over log interval:  3.5533126187324524\n",
            "Epoch:  12 , Batch:  899 , loss avg over log interval:  3.556446475982666\n",
            "Epoch:  12 , Batch:  999 , loss avg over log interval:  3.54851273059845\n",
            "saving model\n",
            "Epoch:  12 , Batch:  1099 , loss avg over log interval:  3.5536370778083803\n",
            "Epoch:  12 , Batch:  1199 , loss avg over log interval:  3.552349154949188\n",
            "saving model\n",
            "Epoch:  12 , Batch:  1299 , loss avg over log interval:  3.557411675453186\n",
            "Epoch:  12 , Batch:  1399 , loss avg over log interval:  3.5463144111633302\n",
            "Epoch:  12 , Batch:  1499 , loss avg over log interval:  3.5458537459373476\n",
            "saving model\n",
            "Epoch:  12 , Batch:  1599 , loss avg over log interval:  3.553555226325989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fy2dqXCRNr7g"
      },
      "source": [
        "# Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "xRFeszKsG6tE",
        "outputId": "9f253f31-284d-4dd5-8c13-f7df948e7f82"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "EPOCH_SAVE_PREFIX = '/content/drive/Shared drives/CIS680 Final Project/models/spatial/'\n",
        "\n",
        "# Device\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "torch.random.manual_seed(1)\n",
        "\n",
        "# Load params\n",
        "last_epoch = 11\n",
        "# \n",
        "# load data\n",
        "network_path = EPOCH_SAVE_PREFIX + 'spatial_epoch' + str(last_epoch)\n",
        "checkpoint = torch.load(network_path)\n",
        "train_loss_list = checkpoint['train_total_loss_list']\n",
        "epoch_loss_list = checkpoint['epoch_total_loss_list']\n",
        "train_counter = checkpoint['train_counter']\n",
        "test_loss_list = checkpoint['test_loss_list']\n",
        "accuracy_list = checkpoint['accuracy_list']\n",
        "epoch_list = np.arange(last_epoch+1)\n",
        "\n",
        "# plots\n",
        "fig = plt.figure()\n",
        "plt.plot(epoch_loss_list, color='blue')\n",
        "plt.legend(['Spatial Train Loss'], loc='upper right')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Total Loss')\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.plot(test_loss_list, color='green')\n",
        "plt.legend(['Spatial Validation Loss'], loc='upper right')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Total Loss')\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.plot(accuracy_list, color='red')\n",
        "plt.legend(['Spatial Validation Accuracy'], loc='lower right')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}