MultiModal.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YSTPd80QoEV",
        "colab_type": "text"
      },
      "source": [
        "# MultiModal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fasDUfxeQnbd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6MGhMIwVFbd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tar -xvf /content/gdrive/My\\ Drive/MultiModal/data/sunrgbd_lite.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzyQ0u9rQmTz",
        "colab_type": "text"
      },
      "source": [
        "# Utils to load and work with RGB-D data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgzxEQCGRXEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import os\n",
        "import sys\n",
        "sys.path.append('/content/gdrive/My Drive/MultiModal')\n",
        "import RGBDutils"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3unXctRXjWJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivUf3pQWR7X7",
        "colab_type": "text"
      },
      "source": [
        "# Baseline, Monomodal\n",
        "Let's see how RGBD and Depth monomodal learning work alone"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzzRG2A4SPJH",
        "colab_type": "text"
      },
      "source": [
        "## Monomodal datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1LwVdG1SE9J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data augmentation and normalization for training\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'test': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n",
        "\n",
        "# Path to the dataset\n",
        "data_dir = 'sunrgbd_lite'\n",
        "\n",
        "# Preparing dataset and dataloaders\n",
        "partitions = ['train', 'val', 'test']\n",
        "rgb_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x, 'rgb'),\n",
        "                                          data_transforms[x])\n",
        "                  for x in partitions}\n",
        "\n",
        "hha_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x, 'hha'),\n",
        "                                          data_transforms[x])\n",
        "                  for x in partitions}\n",
        "\n",
        "rgb_dataloaders = {x: torch.utils.data.DataLoader(rgb_datasets[x], batch_size=4,\n",
        "                                                shuffle=True, num_workers=4)\n",
        "                  for x in partitions}\n",
        "\n",
        "hha_dataloaders = {x: torch.utils.data.DataLoader(hha_datasets[x], batch_size=4,\n",
        "                                                shuffle=True, num_workers=4)\n",
        "                  for x in partitions}\n",
        "rgb_dataset_sizes = {x: len(rgb_datasets[x]) for x in partitions}\n",
        "hha_dataset_sizes = {x: len(hha_datasets[x]) for x in partitions}\n",
        "\n",
        "class_names = rgb_datasets['train'].classes\n",
        "\n",
        "cuda = torch.cuda.is_available()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bx13-A9Saqo",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize some images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnwvqalmSaE8",
        "colab_type": "code",
        "outputId": "0fadd5e6-4408-4d35-c5d7-8937951a5d16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
        "\n",
        "# Get a batch of training data\n",
        "rgb_images, rgb_classes = next(iter(rgb_dataloaders['train']))\n",
        "hha_images, hha_classes = next(iter(hha_dataloaders['train']))\n",
        "rgb_images, rgb_classes = rgb_images[0:4], rgb_classes[0:4]\n",
        "hha_images, hha_classes = hha_images[0:4], hha_classes[0:4]\n",
        "\n",
        "# Make a grid from batch\n",
        "rgb_out = torchvision.utils.make_grid(rgb_images)\n",
        "hha_out = torchvision.utils.make_grid(hha_images)\n",
        "\n",
        "imshow(rgb_out, title=[class_names[x] for x in rgb_classes])\n",
        "imshow(hha_out, title=[class_names[x] for x in hha_classes])"
      ],
      "execution_count": 0,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uYf5cjsWHFj",
        "colab_type": "text"
      },
      "source": [
        "## Monomodal training utilities\n",
        "\n",
        "Breaking it into smaller pieces helps us be more familiar with the actual implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHkP2NG0Slzn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def monomodal_train_epoch(dataloaders, epoch, model, scheduler, optimizer, criterion, best_model_wts, best_acc):\n",
        "  # Each epoch has a training and validation phase\n",
        "  for phase in ['train', 'val']:\n",
        "    print('Phase %s' % phase)\n",
        "    if phase == 'train':\n",
        "      if scheduler != None:\n",
        "        scheduler.step()\n",
        "        model.train(True)  # Set model to training mode\n",
        "      else:\n",
        "        model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for inputs, labels in dataloaders[phase]:\n",
        "      if cuda:\n",
        "        inputs = Variable(inputs.cuda())\n",
        "        labels = Variable(labels.cuda())\n",
        "      else:\n",
        "        inputs, labels = Variable(inputs), Variable(labels)\n",
        "\n",
        "      # zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # forward\n",
        "      outputs = model(inputs)\n",
        "      _, preds = torch.max(outputs.data, 1)\n",
        "      loss = criterion(outputs, labels)\n",
        "\n",
        "      # backward + optimize only if in training phase\n",
        "      if phase == 'train':\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "      # statistics\n",
        "      # running_loss += loss.data[0] * inputs.size(0) # Pytorch 0.4\n",
        "      running_loss += loss.data.item() * inputs.size(0) # Pytorch 1\n",
        "      running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "    epoch_loss = running_loss / rgb_dataset_sizes[phase]\n",
        "    epoch_acc = running_corrects / rgb_dataset_sizes[phase]\n",
        "\n",
        "    print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
        "\n",
        "    # deep copy the model\n",
        "    if phase == 'val' and epoch_acc > best_acc:\n",
        "        best_acc = epoch_acc\n",
        "        best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "  print()\n",
        "  return best_model_wts, best_acc\n",
        "\n",
        "def monomodal_train_model(dataloaders, model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "        best_model_wts, best_acc = monomodal_train_epoch(dataloaders, epoch, model, scheduler, optimizer, criterion, best_model_wts, best_acc)\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWazw2G_WJvE",
        "colab_type": "text"
      },
      "source": [
        "Evaluation model utility"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWJDamiKWNP_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_monomodal_model(dataloaders, model, partition, criterion):\n",
        "    since = time.time()\n",
        "\n",
        "    model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for data in dataloaders[partition]:\n",
        "        # get the inputs\n",
        "        inputs, labels = data\n",
        "        # wrap them in Variable\n",
        "        if cuda:\n",
        "            inputs = Variable(inputs.cuda())\n",
        "            labels = Variable(labels.cuda())\n",
        "        else:\n",
        "            inputs, labels = Variable(inputs), Variable(labels)\n",
        "\n",
        "        # forward\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs.data, 1)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # statistics\n",
        "        # running_loss += loss.data[0] * inputs.size(0) # Pytorch 0.4\n",
        "        running_loss += loss.data.item() * inputs.size(0) # Pytorch 1\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "    test_loss = running_loss / rgb_dataset_sizes[partition]\n",
        "    test_acc = running_corrects / rgb_dataset_sizes[partition]\n",
        "    \n",
        "    print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Tested in {:.0f}m {:.0f}s Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60, test_loss, test_acc))\n",
        "\n",
        "    return test_acc, test_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQQUYVdfWmQH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train For\n",
        "def trainAndEvaluateMonoModal(dalaloaders, initialize_model):\n",
        "  model, criterion, optimizer = initialize_model()\n",
        "  model = monomodal_train_model(dalaloaders, model, criterion, optimizer, None, num_epochs=25)\n",
        "    \n",
        "  # Evaluate\n",
        "  train_acc, _ = evaluate_monomodal_model(dalaloaders, model, 'train', criterion)\n",
        "  val_acc, _ = evaluate_monomodal_model(dalaloaders, model, 'val', criterion)\n",
        "  test_acc, _ = evaluate_monomodal_model(dalaloaders, model, 'test', criterion)\n",
        "  print('Accuracy. Train: %1.2f%% val: %1.2f%% test: %1.2f%%' % \n",
        "        (train_acc*100, val_acc*100, test_acc*100))\n",
        "  return train_acc, val_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QieXA5c0WYGa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def initialize_model_monomodal():\n",
        "  model = models.alexnet(pretrained=True)\n",
        "\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "    \n",
        "  num_classes = len(class_names)\n",
        "  c = model.classifier\n",
        "  num_ftrs = c[6].in_features\n",
        "  model.classifier = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5],nn.Linear(num_ftrs, num_classes))\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate =0.001\n",
        "  perlayer_optim = [\n",
        "    {'params': model.features[0].parameters(), 'lr': 0.00}, # conv1\n",
        "    {'params': model.features[3].parameters(), 'lr': 0.00}, # conv2\n",
        "    {'params': model.features[6].parameters(), 'lr': 0.00}, # conv3\n",
        "    {'params': model.features[8].parameters(), 'lr': 0.00}, # conv4\n",
        "    {'params': model.features[10].parameters(), 'lr': 0.00}, # conv5\n",
        "    {'params': model.classifier[1].parameters(), 'lr': 0.000}, # fc6\n",
        "    {'params': model.classifier[4].parameters(), 'lr': 0.000}, # fc7\n",
        "    {'params': model.classifier[6].parameters(), 'lr': 0.001} # fc8\n",
        "  ]\n",
        "  for param in itertools.chain(model.features[0].parameters(),model.features[3].parameters(),\n",
        "                             model.features[6].parameters(),model.features[8].parameters(),\n",
        "                             model.features[10].parameters(),model.classifier[1].parameters(),\n",
        "                             model.classifier[4].parameters(),model.classifier[6].parameters()):\n",
        "    param.requires_grad = True\n",
        "    \n",
        "  optimizer = torch.optim.Adam(perlayer_optim, lr=learning_rate)\n",
        "\n",
        "  return model, criterion, optimizer\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBpn_jv6b9nF",
        "colab_type": "text"
      },
      "source": [
        "## Train And evaluate RGB 5 runs\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDXLsF9scTQH",
        "colab_type": "code",
        "outputId": "7c9ffe01-1bb0-420c-db53-9013077db575",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c6b851a84879491c9b75f0ff0f06d4e9",
            "4439fb871bf149449a5538c92c8c6bd7",
            "4d3b21a7f9134258ac0e0e92933fe521",
            "6ca7c5aae21246ca99eaeb692e689709",
            "f6594b9a5f384aa3b80ae000e9aabd73",
            "6a979c622cf94f16bf77c4b83292e59b",
            "a1f0077b22794c98b20827df229b0929",
            "2c9f41bb7898465e90550a7a3ad7a8bb"
          ]
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMonoModal(rgb_dataloaders, initialize_model_monomodal), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\" to /root/.cache/torch/checkpoints/alexnet-owt-4df8aa71.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6b851a84879491c9b75f0ff0f06d4e9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=244418560), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8945 Acc: 0.6940\n",
            "Phase val\n",
            "val Loss: 0.8195 Acc: 0.7189\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5077 Acc: 0.8215\n",
            "Phase val\n",
            "val Loss: 0.7621 Acc: 0.7442\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4827 Acc: 0.8403\n",
            "Phase val\n",
            "val Loss: 0.7374 Acc: 0.7638\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3418 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.7115 Acc: 0.7834\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3379 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 0.8985 Acc: 0.7569\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3445 Acc: 0.8658\n",
            "Phase val\n",
            "val Loss: 0.9817 Acc: 0.7373\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3544 Acc: 0.8805\n",
            "Phase val\n",
            "val Loss: 0.7893 Acc: 0.7719\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3094 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.8461 Acc: 0.7903\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2828 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 1.0417 Acc: 0.7523\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2853 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 0.9438 Acc: 0.7650\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3636 Acc: 0.8832\n",
            "Phase val\n",
            "val Loss: 1.0690 Acc: 0.7477\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2858 Acc: 0.8993\n",
            "Phase val\n",
            "val Loss: 1.0241 Acc: 0.7396\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2870 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.0798 Acc: 0.7742\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2861 Acc: 0.9007\n",
            "Phase val\n",
            "val Loss: 1.0482 Acc: 0.7696\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2481 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.1851 Acc: 0.7477\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3110 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.0773 Acc: 0.7788\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2395 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.1400 Acc: 0.7177\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2579 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.0663 Acc: 0.7719\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2743 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.3314 Acc: 0.7546\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2372 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.2257 Acc: 0.7488\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2711 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 1.2305 Acc: 0.7350\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2221 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 1.1963 Acc: 0.7707\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2145 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.3094 Acc: 0.7408\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2621 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.7403 Acc: 0.6751\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2058 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 1.2558 Acc: 0.7362\n",
            "\n",
            "Training complete in 2m 7s\n",
            "Best val Acc: 0.790323\n",
            "\n",
            "Tested in 0m 1s Loss: 0.3216 Acc: 0.8980\n",
            "\n",
            "Tested in 0m 1s Loss: 0.8461 Acc: 0.7903\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0692 Acc: 0.7328\n",
            "Accuracy. Train: 89.80% val: 79.03% test: 73.28%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9101 Acc: 0.6819\n",
            "Phase val\n",
            "val Loss: 0.7378 Acc: 0.7465\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5766 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 0.8392 Acc: 0.7373\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4456 Acc: 0.8443\n",
            "Phase val\n",
            "val Loss: 0.8405 Acc: 0.7327\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3695 Acc: 0.8631\n",
            "Phase val\n",
            "val Loss: 0.9159 Acc: 0.7454\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3700 Acc: 0.8591\n",
            "Phase val\n",
            "val Loss: 1.0161 Acc: 0.7247\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3723 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 0.7505 Acc: 0.7776\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3649 Acc: 0.8725\n",
            "Phase val\n",
            "val Loss: 0.9104 Acc: 0.7730\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3101 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.8824 Acc: 0.7477\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2816 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.9477 Acc: 0.7604\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3066 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.9736 Acc: 0.7546\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3157 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.8565 Acc: 0.7915\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3095 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 1.0385 Acc: 0.7396\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2759 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 1.1688 Acc: 0.7120\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2692 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.9946 Acc: 0.7650\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2980 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 1.1472 Acc: 0.7800\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3202 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.0258 Acc: 0.7719\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2469 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.9916 Acc: 0.7604\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2705 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 1.0103 Acc: 0.7719\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2324 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.9473 Acc: 0.7776\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2993 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 1.1422 Acc: 0.7684\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2760 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 1.1296 Acc: 0.7408\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2556 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.6053 Acc: 0.6694\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1869 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 1.4101 Acc: 0.7293\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2967 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.2123 Acc: 0.7638\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2975 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.4169 Acc: 0.7339\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.791475\n",
            "\n",
            "Tested in 0m 1s Loss: 0.3101 Acc: 0.9020\n",
            "\n",
            "Tested in 0m 1s Loss: 0.8565 Acc: 0.7915\n",
            "\n",
            "Tested in 0m 2s Loss: 1.3829 Acc: 0.6856\n",
            "Accuracy. Train: 90.20% val: 79.15% test: 68.56%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9026 Acc: 0.6913\n",
            "Phase val\n",
            "val Loss: 0.6937 Acc: 0.7615\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5680 Acc: 0.7852\n",
            "Phase val\n",
            "val Loss: 0.8987 Acc: 0.6855\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4451 Acc: 0.8389\n",
            "Phase val\n",
            "val Loss: 1.0837 Acc: 0.7143\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4059 Acc: 0.8658\n",
            "Phase val\n",
            "val Loss: 0.9169 Acc: 0.6982\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4008 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.9587 Acc: 0.7097\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3759 Acc: 0.8644\n",
            "Phase val\n",
            "val Loss: 0.8114 Acc: 0.7984\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3515 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.8644 Acc: 0.7730\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3423 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 0.8961 Acc: 0.7696\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2675 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.9317 Acc: 0.7627\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2621 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.9587 Acc: 0.7396\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2907 Acc: 0.8953\n",
            "Phase val\n",
            "val Loss: 0.9586 Acc: 0.7730\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3420 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 0.8909 Acc: 0.7811\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3284 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 1.2958 Acc: 0.7270\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2979 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.1384 Acc: 0.7811\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2366 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.1628 Acc: 0.7615\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2759 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 1.2271 Acc: 0.7028\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3074 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 1.1429 Acc: 0.7650\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2276 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.0684 Acc: 0.7627\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3205 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.2924 Acc: 0.7500\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2699 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 1.2627 Acc: 0.7350\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2392 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 1.1276 Acc: 0.7707\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2623 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 1.3583 Acc: 0.7696\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2391 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 1.2007 Acc: 0.7800\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2554 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 1.2277 Acc: 0.7465\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2696 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 1.3215 Acc: 0.7488\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.798387\n",
            "\n",
            "Tested in 0m 1s Loss: 0.3122 Acc: 0.8899\n",
            "\n",
            "Tested in 0m 1s Loss: 0.8114 Acc: 0.7984\n",
            "\n",
            "Tested in 0m 2s Loss: 1.2167 Acc: 0.7278\n",
            "Accuracy. Train: 88.99% val: 79.84% test: 72.78%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8950 Acc: 0.6698\n",
            "Phase val\n",
            "val Loss: 0.7481 Acc: 0.7615\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5346 Acc: 0.7946\n",
            "Phase val\n",
            "val Loss: 0.9019 Acc: 0.6935\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4631 Acc: 0.8349\n",
            "Phase val\n",
            "val Loss: 0.8126 Acc: 0.7488\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4448 Acc: 0.8295\n",
            "Phase val\n",
            "val Loss: 0.7744 Acc: 0.7857\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3925 Acc: 0.8658\n",
            "Phase val\n",
            "val Loss: 0.8633 Acc: 0.7396\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3276 Acc: 0.8859\n",
            "Phase val\n",
            "val Loss: 1.0766 Acc: 0.7224\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3307 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 1.0401 Acc: 0.6901\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3365 Acc: 0.8886\n",
            "Phase val\n",
            "val Loss: 1.1197 Acc: 0.7051\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3037 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.0410 Acc: 0.7189\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3615 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 0.9495 Acc: 0.7546\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2525 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.0544 Acc: 0.7247\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2941 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 1.2688 Acc: 0.7154\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1890 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.1914 Acc: 0.7039\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2619 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.0561 Acc: 0.7408\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2283 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.0480 Acc: 0.7419\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2964 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.1878 Acc: 0.7316\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2725 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.2846 Acc: 0.7488\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2524 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.1055 Acc: 0.7846\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2606 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.1602 Acc: 0.7707\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2633 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 1.0983 Acc: 0.7558\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2717 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.3884 Acc: 0.7523\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1922 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 1.2234 Acc: 0.7500\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2493 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 1.2894 Acc: 0.7408\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3002 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.2592 Acc: 0.7362\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2231 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.2350 Acc: 0.7304\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.785714\n",
            "\n",
            "Tested in 0m 1s Loss: 0.3216 Acc: 0.8819\n",
            "\n",
            "Tested in 0m 1s Loss: 0.7744 Acc: 0.7857\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0457 Acc: 0.7316\n",
            "Accuracy. Train: 88.19% val: 78.57% test: 73.16%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9393 Acc: 0.6671\n",
            "Phase val\n",
            "val Loss: 0.7273 Acc: 0.7431\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5718 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 0.8142 Acc: 0.7327\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4306 Acc: 0.8631\n",
            "Phase val\n",
            "val Loss: 0.7376 Acc: 0.7961\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4227 Acc: 0.8537\n",
            "Phase val\n",
            "val Loss: 0.7724 Acc: 0.7753\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4200 Acc: 0.8510\n",
            "Phase val\n",
            "val Loss: 0.9781 Acc: 0.7051\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4071 Acc: 0.8725\n",
            "Phase val\n",
            "val Loss: 0.9285 Acc: 0.7373\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3557 Acc: 0.8859\n",
            "Phase val\n",
            "val Loss: 1.0546 Acc: 0.7177\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3955 Acc: 0.8564\n",
            "Phase val\n",
            "val Loss: 1.1066 Acc: 0.7362\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2452 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 0.9343 Acc: 0.7408\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2693 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 0.8823 Acc: 0.7915\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2313 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.9189 Acc: 0.7776\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2465 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.0892 Acc: 0.7293\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3015 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 1.3615 Acc: 0.7016\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3568 Acc: 0.8819\n",
            "Phase val\n",
            "val Loss: 1.2713 Acc: 0.7120\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2577 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.2649 Acc: 0.7385\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2899 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.3111 Acc: 0.7200\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2704 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.1627 Acc: 0.7592\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2679 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.3331 Acc: 0.7177\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2665 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.1793 Acc: 0.7615\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2431 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.2732 Acc: 0.7224\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2307 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.0888 Acc: 0.7454\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2329 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.1134 Acc: 0.7661\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1715 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 1.5150 Acc: 0.6993\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2205 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.2883 Acc: 0.7316\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2432 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 1.3077 Acc: 0.7753\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.796083\n",
            "\n",
            "Tested in 0m 1s Loss: 0.3732 Acc: 0.8779\n",
            "\n",
            "Tested in 0m 1s Loss: 0.7376 Acc: 0.7961\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0250 Acc: 0.7183\n",
            "Accuracy. Train: 87.79% val: 79.61% test: 71.83%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYpvUaIag-NR",
        "colab_type": "code",
        "outputId": "76df0659-bdf7-47be-fd09-666e25ef964f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB accuracy 88.9932930469513%\n",
            "StDev training RGB accuracy 0.9182627312839031%\n",
            "Mean validation RGB accuracy 79.23963665962219%\n",
            "StDev validation RGB accuracy 0.4456020426005125%\n",
            "Mean test RGB accuracy 71.92186117172241%\n",
            "StDev test RGB accuracy 1.7573904246091843%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71abdKg8cYEn",
        "colab_type": "text"
      },
      "source": [
        "## Train And evaluate HHA 5 runs\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIBUsg14cd5f",
        "colab_type": "code",
        "outputId": "5be0194d-9b4e-462b-84e7-f31d6d818553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMonoModal(hha_dataloaders, initialize_model_monomodal), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1891 Acc: 0.5342\n",
            "Phase val\n",
            "val Loss: 1.2587 Acc: 0.5599\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9373 Acc: 0.6564\n",
            "Phase val\n",
            "val Loss: 1.5276 Acc: 0.4712\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8997 Acc: 0.6604\n",
            "Phase val\n",
            "val Loss: 1.3357 Acc: 0.5230\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8182 Acc: 0.6966\n",
            "Phase val\n",
            "val Loss: 1.4938 Acc: 0.5749\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7601 Acc: 0.7074\n",
            "Phase val\n",
            "val Loss: 1.4682 Acc: 0.5415\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6959 Acc: 0.7570\n",
            "Phase val\n",
            "val Loss: 1.3479 Acc: 0.6037\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7779 Acc: 0.7208\n",
            "Phase val\n",
            "val Loss: 1.6109 Acc: 0.6486\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7403 Acc: 0.7369\n",
            "Phase val\n",
            "val Loss: 1.7938 Acc: 0.5541\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7520 Acc: 0.7396\n",
            "Phase val\n",
            "val Loss: 1.6647 Acc: 0.6002\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5938 Acc: 0.7839\n",
            "Phase val\n",
            "val Loss: 1.6892 Acc: 0.5668\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6454 Acc: 0.7705\n",
            "Phase val\n",
            "val Loss: 1.8114 Acc: 0.5829\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5833 Acc: 0.7946\n",
            "Phase val\n",
            "val Loss: 1.4560 Acc: 0.6406\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6356 Acc: 0.7678\n",
            "Phase val\n",
            "val Loss: 1.6188 Acc: 0.6267\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5996 Acc: 0.7866\n",
            "Phase val\n",
            "val Loss: 1.7021 Acc: 0.5841\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6391 Acc: 0.7799\n",
            "Phase val\n",
            "val Loss: 1.6648 Acc: 0.6187\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5988 Acc: 0.7852\n",
            "Phase val\n",
            "val Loss: 1.6090 Acc: 0.6014\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5240 Acc: 0.7960\n",
            "Phase val\n",
            "val Loss: 1.7707 Acc: 0.6025\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6329 Acc: 0.7799\n",
            "Phase val\n",
            "val Loss: 1.4584 Acc: 0.6359\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5898 Acc: 0.7919\n",
            "Phase val\n",
            "val Loss: 1.7039 Acc: 0.5841\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5443 Acc: 0.8067\n",
            "Phase val\n",
            "val Loss: 1.6028 Acc: 0.6187\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5040 Acc: 0.8040\n",
            "Phase val\n",
            "val Loss: 1.6640 Acc: 0.5680\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5978 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 1.7619 Acc: 0.6094\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5668 Acc: 0.8013\n",
            "Phase val\n",
            "val Loss: 1.6407 Acc: 0.5956\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4921 Acc: 0.8188\n",
            "Phase val\n",
            "val Loss: 1.8678 Acc: 0.6094\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5693 Acc: 0.7973\n",
            "Phase val\n",
            "val Loss: 1.6740 Acc: 0.6256\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.648618\n",
            "\n",
            "Tested in 0m 1s Loss: 0.8622 Acc: 0.7409\n",
            "\n",
            "Tested in 0m 1s Loss: 1.6109 Acc: 0.6486\n",
            "\n",
            "Tested in 0m 2s Loss: 1.9488 Acc: 0.6307\n",
            "Accuracy. Train: 74.09% val: 64.86% test: 63.07%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1716 Acc: 0.5477\n",
            "Phase val\n",
            "val Loss: 1.5599 Acc: 0.5012\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9050 Acc: 0.6631\n",
            "Phase val\n",
            "val Loss: 1.4587 Acc: 0.5265\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8056 Acc: 0.7074\n",
            "Phase val\n",
            "val Loss: 1.3231 Acc: 0.6037\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8255 Acc: 0.6953\n",
            "Phase val\n",
            "val Loss: 1.4370 Acc: 0.5795\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8135 Acc: 0.7114\n",
            "Phase val\n",
            "val Loss: 1.6927 Acc: 0.5242\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7352 Acc: 0.7329\n",
            "Phase val\n",
            "val Loss: 1.6868 Acc: 0.5714\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7631 Acc: 0.7315\n",
            "Phase val\n",
            "val Loss: 1.3156 Acc: 0.6336\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7470 Acc: 0.7262\n",
            "Phase val\n",
            "val Loss: 1.2730 Acc: 0.6659\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6401 Acc: 0.7799\n",
            "Phase val\n",
            "val Loss: 1.4319 Acc: 0.6210\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6481 Acc: 0.7651\n",
            "Phase val\n",
            "val Loss: 1.3483 Acc: 0.5956\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6441 Acc: 0.7517\n",
            "Phase val\n",
            "val Loss: 1.9098 Acc: 0.5012\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6365 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 1.4690 Acc: 0.5991\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6528 Acc: 0.7624\n",
            "Phase val\n",
            "val Loss: 1.5611 Acc: 0.6094\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6429 Acc: 0.7826\n",
            "Phase val\n",
            "val Loss: 1.6117 Acc: 0.5922\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6463 Acc: 0.7597\n",
            "Phase val\n",
            "val Loss: 1.6777 Acc: 0.6083\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6278 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 1.4772 Acc: 0.6336\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5045 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 1.6010 Acc: 0.6152\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5410 Acc: 0.8148\n",
            "Phase val\n",
            "val Loss: 1.5102 Acc: 0.6256\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6502 Acc: 0.7785\n",
            "Phase val\n",
            "val Loss: 1.7163 Acc: 0.5829\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5003 Acc: 0.8188\n",
            "Phase val\n",
            "val Loss: 1.6426 Acc: 0.6325\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5960 Acc: 0.7893\n",
            "Phase val\n",
            "val Loss: 1.6662 Acc: 0.6406\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6072 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 1.4409 Acc: 0.6394\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5994 Acc: 0.7893\n",
            "Phase val\n",
            "val Loss: 1.6050 Acc: 0.6244\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5566 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 1.8353 Acc: 0.5864\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5439 Acc: 0.8107\n",
            "Phase val\n",
            "val Loss: 1.5866 Acc: 0.6348\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.665899\n",
            "\n",
            "Tested in 0m 1s Loss: 0.6361 Acc: 0.7570\n",
            "\n",
            "Tested in 0m 1s Loss: 1.2730 Acc: 0.6659\n",
            "\n",
            "Tested in 0m 2s Loss: 1.4586 Acc: 0.5854\n",
            "Accuracy. Train: 75.70% val: 66.59% test: 58.54%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1726 Acc: 0.5624\n",
            "Phase val\n",
            "val Loss: 1.1756 Acc: 0.5853\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9209 Acc: 0.6510\n",
            "Phase val\n",
            "val Loss: 1.3355 Acc: 0.6014\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8593 Acc: 0.6913\n",
            "Phase val\n",
            "val Loss: 1.4107 Acc: 0.5323\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7868 Acc: 0.7060\n",
            "Phase val\n",
            "val Loss: 1.1870 Acc: 0.6498\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7015 Acc: 0.7342\n",
            "Phase val\n",
            "val Loss: 1.2853 Acc: 0.5991\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7444 Acc: 0.7195\n",
            "Phase val\n",
            "val Loss: 1.8782 Acc: 0.4908\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8181 Acc: 0.7087\n",
            "Phase val\n",
            "val Loss: 1.2686 Acc: 0.6705\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7535 Acc: 0.7423\n",
            "Phase val\n",
            "val Loss: 1.7380 Acc: 0.5634\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6179 Acc: 0.7732\n",
            "Phase val\n",
            "val Loss: 1.5674 Acc: 0.5899\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6273 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 2.1532 Acc: 0.5035\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6067 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 1.4381 Acc: 0.6060\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5924 Acc: 0.7973\n",
            "Phase val\n",
            "val Loss: 1.7588 Acc: 0.5242\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6728 Acc: 0.7678\n",
            "Phase val\n",
            "val Loss: 1.5102 Acc: 0.5772\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6179 Acc: 0.7839\n",
            "Phase val\n",
            "val Loss: 1.8250 Acc: 0.6002\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5750 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 1.6096 Acc: 0.5933\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5845 Acc: 0.7893\n",
            "Phase val\n",
            "val Loss: 1.4975 Acc: 0.5876\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5632 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 1.7852 Acc: 0.5184\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5794 Acc: 0.7772\n",
            "Phase val\n",
            "val Loss: 1.6053 Acc: 0.6256\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5901 Acc: 0.8040\n",
            "Phase val\n",
            "val Loss: 1.8311 Acc: 0.6060\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5533 Acc: 0.7946\n",
            "Phase val\n",
            "val Loss: 1.9784 Acc: 0.5680\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5770 Acc: 0.8094\n",
            "Phase val\n",
            "val Loss: 1.5066 Acc: 0.6843\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5542 Acc: 0.8013\n",
            "Phase val\n",
            "val Loss: 1.6753 Acc: 0.6313\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5571 Acc: 0.8054\n",
            "Phase val\n",
            "val Loss: 2.1588 Acc: 0.5484\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5722 Acc: 0.7973\n",
            "Phase val\n",
            "val Loss: 1.9674 Acc: 0.5150\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4525 Acc: 0.8430\n",
            "Phase val\n",
            "val Loss: 1.4018 Acc: 0.6544\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.684332\n",
            "\n",
            "Tested in 0m 2s Loss: 0.7006 Acc: 0.7597\n",
            "\n",
            "Tested in 0m 1s Loss: 1.5066 Acc: 0.6843\n",
            "\n",
            "Tested in 0m 2s Loss: 1.7841 Acc: 0.6295\n",
            "Accuracy. Train: 75.97% val: 68.43% test: 62.95%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1936 Acc: 0.5490\n",
            "Phase val\n",
            "val Loss: 1.4610 Acc: 0.5150\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9192 Acc: 0.6577\n",
            "Phase val\n",
            "val Loss: 1.4902 Acc: 0.5207\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8616 Acc: 0.6725\n",
            "Phase val\n",
            "val Loss: 1.7885 Acc: 0.5023\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8287 Acc: 0.6779\n",
            "Phase val\n",
            "val Loss: 1.6588 Acc: 0.5553\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6969 Acc: 0.7235\n",
            "Phase val\n",
            "val Loss: 1.4016 Acc: 0.5657\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6497 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 1.3308 Acc: 0.6187\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6753 Acc: 0.7383\n",
            "Phase val\n",
            "val Loss: 1.5217 Acc: 0.5760\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6576 Acc: 0.7678\n",
            "Phase val\n",
            "val Loss: 1.5533 Acc: 0.5541\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6306 Acc: 0.7745\n",
            "Phase val\n",
            "val Loss: 1.4042 Acc: 0.6267\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6985 Acc: 0.7651\n",
            "Phase val\n",
            "val Loss: 1.5709 Acc: 0.5968\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6163 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 2.1400 Acc: 0.4551\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6458 Acc: 0.7611\n",
            "Phase val\n",
            "val Loss: 1.7889 Acc: 0.5864\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5880 Acc: 0.7745\n",
            "Phase val\n",
            "val Loss: 1.4898 Acc: 0.6083\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6501 Acc: 0.7597\n",
            "Phase val\n",
            "val Loss: 1.7193 Acc: 0.5795\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5251 Acc: 0.7933\n",
            "Phase val\n",
            "val Loss: 1.8533 Acc: 0.5634\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6024 Acc: 0.7772\n",
            "Phase val\n",
            "val Loss: 1.7159 Acc: 0.5899\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6002 Acc: 0.7758\n",
            "Phase val\n",
            "val Loss: 2.2347 Acc: 0.4850\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5600 Acc: 0.7933\n",
            "Phase val\n",
            "val Loss: 1.7221 Acc: 0.6060\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5882 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 1.7539 Acc: 0.5369\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6456 Acc: 0.7866\n",
            "Phase val\n",
            "val Loss: 1.5309 Acc: 0.6267\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5630 Acc: 0.8067\n",
            "Phase val\n",
            "val Loss: 2.0740 Acc: 0.5288\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6031 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 1.7262 Acc: 0.6175\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6021 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 1.5538 Acc: 0.6371\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5240 Acc: 0.8228\n",
            "Phase val\n",
            "val Loss: 1.5954 Acc: 0.6532\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4887 Acc: 0.8174\n",
            "Phase val\n",
            "val Loss: 1.9076 Acc: 0.5783\n",
            "\n",
            "Training complete in 2m 10s\n",
            "Best val Acc: 0.653226\n",
            "\n",
            "Tested in 0m 2s Loss: 0.5229 Acc: 0.8376\n",
            "\n",
            "Tested in 0m 1s Loss: 1.5954 Acc: 0.6532\n",
            "\n",
            "Tested in 0m 2s Loss: 1.6983 Acc: 0.6112\n",
            "Accuracy. Train: 83.76% val: 65.32% test: 61.12%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1838 Acc: 0.5718\n",
            "Phase val\n",
            "val Loss: 1.1807 Acc: 0.5922\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9259 Acc: 0.6537\n",
            "Phase val\n",
            "val Loss: 1.4445 Acc: 0.5323\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8327 Acc: 0.6765\n",
            "Phase val\n",
            "val Loss: 1.3341 Acc: 0.5622\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7882 Acc: 0.7114\n",
            "Phase val\n",
            "val Loss: 1.3908 Acc: 0.6371\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8199 Acc: 0.7087\n",
            "Phase val\n",
            "val Loss: 1.5321 Acc: 0.5173\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7586 Acc: 0.7302\n",
            "Phase val\n",
            "val Loss: 1.4447 Acc: 0.5933\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7613 Acc: 0.7275\n",
            "Phase val\n",
            "val Loss: 1.5300 Acc: 0.6164\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7515 Acc: 0.7342\n",
            "Phase val\n",
            "val Loss: 1.4886 Acc: 0.6198\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6698 Acc: 0.7544\n",
            "Phase val\n",
            "val Loss: 1.4972 Acc: 0.6578\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6381 Acc: 0.7651\n",
            "Phase val\n",
            "val Loss: 1.8034 Acc: 0.5449\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6459 Acc: 0.7691\n",
            "Phase val\n",
            "val Loss: 1.4888 Acc: 0.6578\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6038 Acc: 0.7745\n",
            "Phase val\n",
            "val Loss: 1.6380 Acc: 0.5749\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6681 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 1.5931 Acc: 0.5806\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6169 Acc: 0.7893\n",
            "Phase val\n",
            "val Loss: 1.6936 Acc: 0.6152\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5813 Acc: 0.7946\n",
            "Phase val\n",
            "val Loss: 1.7347 Acc: 0.5853\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6198 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 1.7375 Acc: 0.5887\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5491 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 1.7775 Acc: 0.5991\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6141 Acc: 0.7906\n",
            "Phase val\n",
            "val Loss: 2.2359 Acc: 0.5357\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5628 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 1.5575 Acc: 0.6325\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5309 Acc: 0.8134\n",
            "Phase val\n",
            "val Loss: 1.6711 Acc: 0.5703\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5137 Acc: 0.8040\n",
            "Phase val\n",
            "val Loss: 1.5418 Acc: 0.6118\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5964 Acc: 0.7826\n",
            "Phase val\n",
            "val Loss: 1.7778 Acc: 0.6025\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5623 Acc: 0.8094\n",
            "Phase val\n",
            "val Loss: 1.9720 Acc: 0.5818\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5557 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 1.6521 Acc: 0.6187\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5054 Acc: 0.8188\n",
            "Phase val\n",
            "val Loss: 1.8734 Acc: 0.6221\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.657834\n",
            "\n",
            "Tested in 0m 2s Loss: 0.6379 Acc: 0.7664\n",
            "\n",
            "Tested in 0m 1s Loss: 1.4972 Acc: 0.6578\n",
            "\n",
            "Tested in 0m 2s Loss: 1.7709 Acc: 0.6118\n",
            "Accuracy. Train: 76.64% val: 65.78% test: 61.18%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuAJcYblhCcf",
        "colab_type": "code",
        "outputId": "64e39411-db6f-4148-9f67-36bd75c11694",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training HHA accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training HHA accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation HHA accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation HHA accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test HHA accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test HHA accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training HHA accuracy 77.23489999771118%\n",
            "StDev training HHA accuracy 3.367602452635765%\n",
            "Mean validation HHA accuracy 66.19815230369568%\n",
            "StDev validation HHA accuracy 1.254861243069172%\n",
            "Mean test HHA accuracy 61.37365698814392%\n",
            "StDev test HHA accuracy 1.644117571413517%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oiLDN5zkzMB",
        "colab_type": "text"
      },
      "source": [
        "# Baseline, multimodal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rsq5BVtaBu9",
        "colab_type": "text"
      },
      "source": [
        "## Multimodal datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31y6wIyuk6YJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data augmentation and normalization for training\n",
        "RGB_AVG = [0.485, 0.456, 0.406] # Default ImageNet ILSRVC2012\n",
        "RGB_STD = [0.229, 0.224, 0.225] # Default ImageNet ILSRVC2012\n",
        "DEPTH_AVG = [0.485, 0.456, 0.406] # Default ImageNet ILSRVC2012\n",
        "DEPTH_STD = [0.229, 0.224, 0.225] # Default ImageNet ILSRVC2012\n",
        "data_transforms = {\n",
        "    'train': RGBDutils.Compose([\n",
        "        RGBDutils.RandomResizedCrop(227),\n",
        "        RGBDutils.RandomHorizontalFlip(),\n",
        "        RGBDutils.ToTensor(),\n",
        "        RGBDutils.Normalize(RGB_AVG, RGB_STD, DEPTH_AVG, DEPTH_STD)\n",
        "    ]),\n",
        "    'val': RGBDutils.Compose([\n",
        "        RGBDutils.Resize(256),\n",
        "        RGBDutils.CenterCrop(227),\n",
        "        RGBDutils.ToTensor(),\n",
        "        RGBDutils.Normalize(RGB_AVG, RGB_STD, DEPTH_AVG, DEPTH_STD)\n",
        "    ]),\n",
        "    'test': RGBDutils.Compose([\n",
        "        RGBDutils.Resize(256),\n",
        "        RGBDutils.CenterCrop(227),\n",
        "        RGBDutils.ToTensor(),\n",
        "        RGBDutils.Normalize(RGB_AVG, RGB_STD, DEPTH_AVG, DEPTH_STD)\n",
        "    ]),\n",
        "}\n",
        "\n",
        "# Path to the dataset\n",
        "# data_dir = '/home/mcv/datasets/sunrgbd_lite'\n",
        "data_dir = 'sunrgbd_lite'\n",
        "\n",
        "# Preparing dataset and dataloaders\n",
        "partitions = ['train', 'val', 'test']\n",
        "multimodal_datasets = {x: RGBDutils.ImageFolder(os.path.join(data_dir, x),\n",
        "                                          data_transforms[x])\n",
        "                    for x in partitions}\n",
        "\n",
        "multimodal_dataloaders = {x: torch.utils.data.DataLoader(multimodal_datasets[x], batch_size=64,\n",
        "                                             shuffle=True, num_workers=4)\n",
        "                    for x in partitions}\n",
        "\n",
        "dataset_sizes = {x: len(multimodal_datasets[x]) for x in partitions}\n",
        "class_names = multimodal_datasets['train'].classes\n",
        "\n",
        "cuda = torch.cuda.is_available()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fjc45rdxoNKV",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize some images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iE_GrGUUlQSV",
        "colab_type": "code",
        "outputId": "bb707c6d-bf59-4f78-ac1e-8c06a478ccc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "# Get a batch of training data and visualize the first four pairs\n",
        "inputsRGB, inputsDepth, classes = next(iter(multimodal_dataloaders['train']))\n",
        "inputsRGB, inputsDepth, classes = inputsRGB[0:4], inputsDepth[0:4], classes[0:4]\n",
        "\n",
        "# Make a grid from batch\n",
        "outRGB = torchvision.utils.make_grid(inputsRGB)\n",
        "outDepth = torchvision.utils.make_grid(inputsDepth)\n",
        "\n",
        "RGBDutils.imshow(outRGB, outDepth, title=[class_names[x] for x in classes],concat_vert=True)"
      ],
      "execution_count": 0,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytN7YQNmlhd7",
        "colab_type": "text"
      },
      "source": [
        "## Training multimodal utility"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0xl1AOOlkLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def multimodal_train_epoch(dataloaders, epoch, model, scheduler, optimizer, criterion, best_model_wts, best_acc):\n",
        "  # Each epoch has a training and validation phase\n",
        "  for phase in ['train', 'val']:\n",
        "    print('Phase %s' % phase)\n",
        "    if phase == 'train':\n",
        "      if scheduler != None:\n",
        "        scheduler.step()\n",
        "        model.train(True)  # Set model to training mode\n",
        "      else:\n",
        "        model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for inputs_rgb, inputs_hha, labels in dataloaders[phase]:\n",
        "      if cuda:\n",
        "        inputs_rgb = Variable(inputs_rgb.cuda())\n",
        "        inputs_hha = Variable(inputs_hha.cuda())\n",
        "        labels = Variable(labels.cuda())\n",
        "      else:\n",
        "        inputs_rgb, inputs_hha, labels = Variable(inputs_hha), Variable(inputs_hha), Variable(labels)\n",
        "\n",
        "      # zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # forward\n",
        "      outputs = model((inputs_rgb, inputs_hha))\n",
        "      _, preds = torch.max(outputs.data, 1)\n",
        "      loss = criterion(outputs, labels)\n",
        "\n",
        "      # backward + optimize only if in training phase\n",
        "      if phase == 'train':\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "      # statistics\n",
        "      # running_loss += loss.data[0] * inputs.size(0) # Pytorch 0.4\n",
        "      running_loss += loss.data.item() * inputs_rgb.size(0) # Pytorch 1\n",
        "      running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "    epoch_loss = running_loss / dataset_sizes[phase]\n",
        "    epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "\n",
        "    print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
        "\n",
        "    # deep copy the model\n",
        "    if phase == 'val' and epoch_acc > best_acc:\n",
        "        best_acc = epoch_acc\n",
        "        best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "  print()\n",
        "  return best_model_wts, best_acc\n",
        "\n",
        "def multimodal_train_model(dataloaders, model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "        best_model_wts, best_acc = multimodal_train_epoch(dataloaders, epoch, model, scheduler, optimizer, criterion, best_model_wts, best_acc)\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfq3MhUZoSY7",
        "colab_type": "text"
      },
      "source": [
        "Evaluation multimodal utility"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBcI5EO8mwzh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_multimodal_model(dataloaders, model, partition, criterion):\n",
        "    since = time.time()\n",
        "\n",
        "    model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for data in dataloaders[partition]:\n",
        "        # get the inputs\n",
        "        inputs_rgb, inputs_hha, labels = data\n",
        "        # wrap them in Variable\n",
        "        if cuda:\n",
        "            inputs_rgb = Variable(inputs_rgb.cuda())\n",
        "            inputs_hha = Variable(inputs_hha.cuda())\n",
        "            labels = Variable(labels.cuda())\n",
        "        else:\n",
        "            inputs_rgb, inputs_hha, labels = Variable(inputs_hha), Variable(inputs_hha), Variable(labels)\n",
        "\n",
        "        # forward\n",
        "        outputs = model((inputs_rgb, inputs_hha))\n",
        "        _, preds = torch.max(outputs.data, 1)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # statistics\n",
        "        # running_loss += loss.data[0] * inputs_rgb.size(0) # Pytorch 0.4\n",
        "        running_loss += loss.data.item() * inputs_rgb.size(0) # Pytorch 1\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "    test_loss = running_loss / dataset_sizes[partition]\n",
        "    test_acc = running_corrects / dataset_sizes[partition]\n",
        "    \n",
        "    print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Tested in {:.0f}m {:.0f}s Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60, test_loss, test_acc))\n",
        "\n",
        "    return test_acc, test_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcKp2tUfnCcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train For\n",
        "def trainAndEvaluateMultiModal(dalaloaders, initialize_model, num_epochs=25):\n",
        "  model, criterion, optimizer = initialize_model()\n",
        "  model = multimodal_train_model(dalaloaders, model, criterion, optimizer, None, num_epochs=num_epochs)\n",
        "    \n",
        "  # Evaluate\n",
        "  train_acc, _ = evaluate_multimodal_model(dalaloaders, model, 'train', criterion)\n",
        "  val_acc, _ = evaluate_multimodal_model(dalaloaders, model, 'val', criterion)\n",
        "  test_acc, _ = evaluate_multimodal_model(dalaloaders, model, 'test', criterion)\n",
        "  print('Accuracy. Train: %1.2f%% val: %1.2f%% test: %1.2f%%' % \n",
        "        (train_acc*100, val_acc*100, test_acc*100))\n",
        "  return train_acc, val_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eX_g3KySnPg8",
        "colab_type": "text"
      },
      "source": [
        "## Class to implement MultiModal network with two branches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQMGfnz1nNQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In PyTorch every network is implementd as a nn.Module\n",
        "class RGBDnet(nn.Module):\n",
        "    # The parameters are initialized in __init__(self, ...)\n",
        "    def __init__(self, rgb_branch, hha_branch, num_classes):\n",
        "        super(RGBDnet, self).__init__()\n",
        "        \n",
        "        # RGB branch\n",
        "        model_rgb = rgb_branch\n",
        "        self.rgb_convs = model_rgb.features\n",
        "        c = model_rgb.classifier\n",
        "        self.rgb_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        num_ftrs_rgb = c[4].out_features\n",
        "\n",
        "        # HHA branch\n",
        "        model_hha = hha_branch\n",
        "        self.hha_convs = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        self.hha_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        f = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        num_ftrs_hha = c[4].out_features\n",
        "\n",
        "        # Classifier\n",
        "        self.classifier = nn.Linear(num_ftrs_rgb+num_ftrs_hha, num_classes)\n",
        "\n",
        "    # The data flow is defined in forward. No need to specify backward operations (PyTorch takes care of them)\n",
        "    def forward(self, x):\n",
        "        x_rgb = self.rgb_convs(x[0])\n",
        "        x_rgb = x_rgb.view(x_rgb.size(0), 256 * 6 * 6)\n",
        "        x_hha = self.hha_convs(x[1])\n",
        "        x_hha = x_hha.view(x_hha.size(0), 256 * 6 * 6)\n",
        "        x_rgb = self.rgb_fcs(x_rgb)\n",
        "        x_hha = self.hha_fcs(x_hha)\n",
        "        x = torch.cat((x_rgb, x_hha), 1)\n",
        "        x = self.classifier(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS1oB4R_nN_w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision.models\n",
        "def initialize_model_rgbd():\n",
        "  # Instantiate the model\n",
        "  num_classes = len(class_names)\n",
        "  model = RGBDnet(rgb_branch=models.alexnet(pretrained=True),\n",
        "                hha_branch=models.alexnet(pretrained=True), \n",
        "                num_classes=num_classes)\n",
        "\n",
        "  # Here we define the learning rate\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate = 0.001\n",
        "    \n",
        "  perlayer_optim = [\n",
        "    {'params': model.rgb_convs[0].parameters(), 'lr': 0.00}, # conv1 RGB\n",
        "    {'params': model.rgb_convs[3].parameters(), 'lr': 0.00}, # conv2 RGB\n",
        "    {'params': model.rgb_convs[6].parameters(), 'lr': 0.00}, # conv3 RGB\n",
        "    {'params': model.rgb_convs[8].parameters(), 'lr': 0.00}, # conv4 RGB\n",
        "    {'params': model.rgb_convs[10].parameters(), 'lr': 0.00}, # conv5 RGB\n",
        "    {'params': model.rgb_fcs[1].parameters(), 'lr': 0.00}, # fc6 RGB\n",
        "    {'params': model.rgb_fcs[4].parameters(), 'lr': 0.00}, # fc7 RGB\n",
        "    {'params': model.hha_convs[0].parameters(), 'lr': 0.00}, # conv1 HHA\n",
        "    {'params': model.hha_convs[3].parameters(), 'lr': 0.00}, # conv2 HHA\n",
        "    {'params': model.hha_convs[6].parameters(), 'lr': 0.00}, # conv3 HHA\n",
        "    {'params': model.hha_convs[8].parameters(), 'lr': 0.00}, # conv4 HHA\n",
        "    {'params': model.hha_convs[10].parameters(), 'lr': 0.00}, # conv5 HHA\n",
        "    {'params': model.hha_fcs[1].parameters(), 'lr': 0.00}, # fc6 HHA\n",
        "    {'params': model.hha_fcs[4].parameters(), 'lr': 0.00}, # fc7 HHA\n",
        "    {'params': model.classifier.parameters(), 'lr': 0.001} # fc8\n",
        "  ]\n",
        "  for param in itertools.chain(model.rgb_convs[0].parameters(),model.rgb_convs[3].parameters(),\n",
        "                             model.rgb_convs[6].parameters(),model.rgb_convs[8].parameters(),\n",
        "                             model.rgb_convs[10].parameters(),model.rgb_fcs[1].parameters(),\n",
        "                             model.rgb_fcs[4].parameters(),\n",
        "                             model.hha_convs[0].parameters(),model.hha_convs[3].parameters(),\n",
        "                             model.hha_convs[6].parameters(),model.hha_convs[8].parameters(),\n",
        "                             model.hha_convs[10].parameters(),model.hha_fcs[1].parameters(),\n",
        "                             model.hha_fcs[4].parameters(),\n",
        "                             model.classifier.parameters()):\n",
        "    param.requires_grad = True\n",
        "    \n",
        "    \n",
        "  optimizer = torch.optim.Adam(perlayer_optim, lr=learning_rate)\n",
        "\n",
        "  return model, criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-Y3DhldaUBT",
        "colab_type": "text"
      },
      "source": [
        "## Train And evaluate RGB-D 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7L3gtBfngF6",
        "colab_type": "code",
        "outputId": "23242993-c5e4-4bab-fc49-98c700b3b1e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMultiModal(multimodal_dataloaders, initialize_model_rgbd), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9605 Acc: 0.6201\n",
            "Phase val\n",
            "val Loss: 0.7204 Acc: 0.7350\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4970 Acc: 0.8054\n",
            "Phase val\n",
            "val Loss: 0.6755 Acc: 0.7650\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4234 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.5849 Acc: 0.8088\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3332 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.6695 Acc: 0.7800\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3575 Acc: 0.8644\n",
            "Phase val\n",
            "val Loss: 0.6342 Acc: 0.7800\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2948 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 0.5879 Acc: 0.7984\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2323 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6534 Acc: 0.7730\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2235 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 0.5946 Acc: 0.8088\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2354 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 0.6223 Acc: 0.7892\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2093 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.7248 Acc: 0.7500\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1981 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6134 Acc: 0.8088\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1888 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6764 Acc: 0.7753\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1887 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6408 Acc: 0.7949\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1987 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6677 Acc: 0.7903\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1761 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6306 Acc: 0.7857\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1602 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.7225 Acc: 0.7684\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1408 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.6199 Acc: 0.7880\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1682 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.7038 Acc: 0.7627\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1488 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6335 Acc: 0.8065\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1845 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 0.7013 Acc: 0.7684\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1273 Acc: 0.9678\n",
            "Phase val\n",
            "val Loss: 0.7206 Acc: 0.7753\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1586 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6908 Acc: 0.7776\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1428 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6532 Acc: 0.7857\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1384 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.7533 Acc: 0.7535\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1510 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6931 Acc: 0.7857\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.808756\n",
            "\n",
            "Tested in 0m 3s Loss: 0.3348 Acc: 0.8658\n",
            "\n",
            "Tested in 0m 2s Loss: 0.5849 Acc: 0.8088\n",
            "\n",
            "Tested in 0m 4s Loss: 0.7959 Acc: 0.7473\n",
            "Accuracy. Train: 86.58% val: 80.88% test: 74.73%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9571 Acc: 0.6416\n",
            "Phase val\n",
            "val Loss: 0.7164 Acc: 0.7615\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4792 Acc: 0.8268\n",
            "Phase val\n",
            "val Loss: 0.6706 Acc: 0.7650\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4112 Acc: 0.8403\n",
            "Phase val\n",
            "val Loss: 0.5619 Acc: 0.8018\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3603 Acc: 0.8738\n",
            "Phase val\n",
            "val Loss: 0.5586 Acc: 0.8203\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3114 Acc: 0.8859\n",
            "Phase val\n",
            "val Loss: 0.5983 Acc: 0.8168\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2604 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 0.6197 Acc: 0.8041\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2702 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.6803 Acc: 0.7949\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2341 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 0.6595 Acc: 0.7880\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1937 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6695 Acc: 0.7800\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2282 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.5958 Acc: 0.8111\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1921 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6013 Acc: 0.7938\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1915 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6334 Acc: 0.8053\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1706 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.5897 Acc: 0.8007\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1929 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 0.6507 Acc: 0.8007\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1795 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6546 Acc: 0.7869\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1800 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6439 Acc: 0.7857\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1715 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6829 Acc: 0.7972\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1677 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.7313 Acc: 0.7742\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1551 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.7351 Acc: 0.7546\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1435 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7116 Acc: 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1223 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.7235 Acc: 0.7627\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1143 Acc: 0.9638\n",
            "Phase val\n",
            "val Loss: 0.7039 Acc: 0.7742\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1605 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6548 Acc: 0.7903\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1590 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.7328 Acc: 0.7811\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1312 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.6548 Acc: 0.7972\n",
            "\n",
            "Training complete in 2m 7s\n",
            "Best val Acc: 0.820276\n",
            "\n",
            "Tested in 0m 2s Loss: 0.3176 Acc: 0.8859\n",
            "\n",
            "Tested in 0m 2s Loss: 0.5586 Acc: 0.8203\n",
            "\n",
            "Tested in 0m 4s Loss: 0.7544 Acc: 0.7606\n",
            "Accuracy. Train: 88.59% val: 82.03% test: 76.06%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1464 Acc: 0.5678\n",
            "Phase val\n",
            "val Loss: 0.7355 Acc: 0.7638\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5911 Acc: 0.7839\n",
            "Phase val\n",
            "val Loss: 0.7365 Acc: 0.7028\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4316 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.7046 Acc: 0.7592\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3549 Acc: 0.8752\n",
            "Phase val\n",
            "val Loss: 0.6187 Acc: 0.7765\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3135 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.5904 Acc: 0.7938\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2887 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.6403 Acc: 0.7719\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2736 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 0.6130 Acc: 0.7730\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2412 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.7160 Acc: 0.7558\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2128 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6537 Acc: 0.7846\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2055 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6290 Acc: 0.7869\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2097 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.6513 Acc: 0.7915\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2015 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6272 Acc: 0.7857\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2253 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.7181 Acc: 0.7684\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1922 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 0.6946 Acc: 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1807 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6744 Acc: 0.7707\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1846 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.7770 Acc: 0.7684\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1843 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.7390 Acc: 0.7465\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1475 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6799 Acc: 0.7869\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1627 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.7544 Acc: 0.7454\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1865 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.7117 Acc: 0.7546\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1393 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.7211 Acc: 0.7846\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1518 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.8354 Acc: 0.7431\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1443 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7797 Acc: 0.7500\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1557 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.7171 Acc: 0.8030\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1516 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.7436 Acc: 0.7730\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.802995\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1475 Acc: 0.9463\n",
            "\n",
            "Tested in 0m 2s Loss: 0.7171 Acc: 0.8030\n",
            "\n",
            "Tested in 0m 4s Loss: 0.8991 Acc: 0.7675\n",
            "Accuracy. Train: 94.63% val: 80.30% test: 76.75%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.0205 Acc: 0.6537\n",
            "Phase val\n",
            "val Loss: 0.7970 Acc: 0.6889\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5370 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 0.7815 Acc: 0.7408\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3809 Acc: 0.8523\n",
            "Phase val\n",
            "val Loss: 0.6250 Acc: 0.7661\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3334 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.5954 Acc: 0.7857\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3316 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.6196 Acc: 0.7961\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2600 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 0.6595 Acc: 0.7765\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2248 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6225 Acc: 0.7834\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2100 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6422 Acc: 0.7684\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2225 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6106 Acc: 0.7903\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1747 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6497 Acc: 0.7869\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1925 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6158 Acc: 0.8053\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1761 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6668 Acc: 0.7880\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1788 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.7446 Acc: 0.7604\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1657 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6646 Acc: 0.7961\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1433 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.6614 Acc: 0.7995\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1749 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.7717 Acc: 0.7465\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1589 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6365 Acc: 0.7915\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1638 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6574 Acc: 0.8076\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1663 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.7256 Acc: 0.7661\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1542 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6246 Acc: 0.8018\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1571 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6579 Acc: 0.7696\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1792 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.7318 Acc: 0.7869\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1532 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6775 Acc: 0.7938\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1521 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.7109 Acc: 0.7880\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1224 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6846 Acc: 0.7857\n",
            "\n",
            "Training complete in 2m 8s\n",
            "Best val Acc: 0.807604\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1397 Acc: 0.9570\n",
            "\n",
            "Tested in 0m 2s Loss: 0.6574 Acc: 0.8076\n",
            "\n",
            "Tested in 0m 4s Loss: 0.8871 Acc: 0.7543\n",
            "Accuracy. Train: 95.70% val: 80.76% test: 75.43%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9633 Acc: 0.6336\n",
            "Phase val\n",
            "val Loss: 0.6075 Acc: 0.7730\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5375 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 0.6341 Acc: 0.7650\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4044 Acc: 0.8617\n",
            "Phase val\n",
            "val Loss: 0.5999 Acc: 0.7949\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3551 Acc: 0.8725\n",
            "Phase val\n",
            "val Loss: 0.6331 Acc: 0.7903\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2744 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 0.6666 Acc: 0.7523\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2873 Acc: 0.8993\n",
            "Phase val\n",
            "val Loss: 0.5957 Acc: 0.7972\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2429 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 0.5665 Acc: 0.7972\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2224 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 0.6002 Acc: 0.7846\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2173 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6398 Acc: 0.7742\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2399 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 0.6942 Acc: 0.7730\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2156 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.6389 Acc: 0.7800\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2065 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.7189 Acc: 0.7500\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1599 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6499 Acc: 0.7846\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1650 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6326 Acc: 0.7880\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1722 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6368 Acc: 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1669 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6488 Acc: 0.7811\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1506 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6621 Acc: 0.7869\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1409 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6327 Acc: 0.7903\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1393 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6665 Acc: 0.7788\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1551 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.7203 Acc: 0.7673\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1205 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6143 Acc: 0.8018\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1505 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.7299 Acc: 0.7684\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1255 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.6628 Acc: 0.8076\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1489 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6234 Acc: 0.7984\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1405 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6989 Acc: 0.7834\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.807604\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1485 Acc: 0.9409\n",
            "\n",
            "Tested in 0m 2s Loss: 0.6628 Acc: 0.8076\n",
            "\n",
            "Tested in 0m 4s Loss: 0.9536 Acc: 0.7599\n",
            "Accuracy. Train: 94.09% val: 80.76% test: 75.99%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7FaI3HxntaQ",
        "colab_type": "code",
        "outputId": "51f4cca4-5c03-4c96-c5f4-5b9a926ac798",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB-D accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB-D accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB-D accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB-D accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB-D accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB-D accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB-D accuracy 91.91946983337402%\n",
            "StDev training RGB-D accuracy 3.633992001414299%\n",
            "Mean validation RGB-D accuracy 80.94469904899597%\n",
            "StDev validation RGB-D accuracy 0.5764981266111135%\n",
            "Mean test RGB-D accuracy 75.79079866409302%\n",
            "StDev test RGB-D accuracy 0.6756109185516834%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23BGq02iOlFV",
        "colab_type": "text"
      },
      "source": [
        "# Comparison Monomodal vs MultiModal performance\n",
        "\n",
        "\n",
        "Experiment Name | Train Accuracy (Avg/StdDev)| Val Accuracy (Avg/StdDev)| Test accuracy (Avg/StdDev)|\n",
        "--- | --- | --- | ---\n",
        "RGB | 88.99 % / 0.92 % | 79.24 % / 0.44 % | 71.92 % / 1.76 %\n",
        "HHA |  77.23 % / 3.36 % | 66.20 % / 1.25 % | 61.37 % / 1.64 %\n",
        "RGB-D | 91.92 % / 3.64 % | 80.94% / 0.58 % | 75.79 % / 0.68 %\n",
        "\n",
        "The results are more or less expected, the model that uses only RGB information outperforms from HHA that uses only depth info. This might be due to the fact that RGB info is more important for the task or that the pretrained AlexNet models they are based on have been trained on an RGB domain.\n",
        "\n",
        "But the combination of those with the multimodal deep learning approach outperforms them proving the use of multiple modalities to solve a classification task, although one may expect to see a larger impact."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-h_EUjFadxW",
        "colab_type": "text"
      },
      "source": [
        "#Further steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3hGMHPav_Ut",
        "colab_type": "text"
      },
      "source": [
        "So far we have seen how monomodal with RGB and HHA works and compared with multimodal scheme, but the multimodal was made by just concatenating the output of the two branches before a classification layer.\n",
        "\n",
        "Next, we pretend to test some different approaches:\n",
        "*   Fine tune all layers\n",
        "*   Use AlexNet pretrained on a different dataset (Places365)\n",
        "*   Use different architectures instead of AlexNet\n",
        "\n",
        "*   Align to joint space\n",
        "*   Destillation from RGB (domain closer to where pretrained model is trained) to HHA \n",
        "*   Fusion at different layers\n",
        "*   Depth-wise convolutions\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXwp3AWkC2YS",
        "colab_type": "text"
      },
      "source": [
        "## Fine-tuning layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0vH5ey9C1ve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision.models\n",
        "def initialize_model_rgbd_finetune():\n",
        "  # Instantiate the model\n",
        "  num_classes = len(class_names)\n",
        "  model = RGBDnet(rgb_branch=models.alexnet(pretrained=True),\n",
        "                  hha_branch=models.alexnet(pretrained=True), \n",
        "                  num_classes=num_classes)\n",
        "\n",
        "  # Here we define the learning rate\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate = 0.001\n",
        "\n",
        "  for param in model.rgb_convs[10:].parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.hha_convs[10:].parameters():\n",
        "    param.requires_grad = True\n",
        "    \n",
        "  for param in model.rgb_fcs.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.hha_fcs.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.classifier.parameters():\n",
        "    param.requires_grad = True\n",
        "    \n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "  return model, criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRDCMUnqcQFY",
        "colab_type": "text"
      },
      "source": [
        "### Train and evaluate network with fine tuned layers 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIXLwVNSC9wO",
        "colab_type": "code",
        "outputId": "f384ccea-6be6-4343-e82c-da36a67350f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMultiModal(multimodal_dataloaders, initialize_model_rgbd_finetune, num_epochs=35), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 2.0165 Acc: 0.4523\n",
            "Phase val\n",
            "val Loss: 0.9998 Acc: 0.7028\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8134 Acc: 0.7020\n",
            "Phase val\n",
            "val Loss: 0.9049 Acc: 0.6889\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6127 Acc: 0.7799\n",
            "Phase val\n",
            "val Loss: 0.6767 Acc: 0.7926\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4436 Acc: 0.8376\n",
            "Phase val\n",
            "val Loss: 0.6625 Acc: 0.7535\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3868 Acc: 0.8510\n",
            "Phase val\n",
            "val Loss: 0.7139 Acc: 0.7995\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4160 Acc: 0.8671\n",
            "Phase val\n",
            "val Loss: 0.8071 Acc: 0.7707\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3117 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.8999 Acc: 0.7385\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3058 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.1489 Acc: 0.6832\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3015 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.7081 Acc: 0.7892\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1898 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.9012 Acc: 0.8134\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2622 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.9803 Acc: 0.7592\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2215 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 0.9693 Acc: 0.7477\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2071 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 1.0549 Acc: 0.7465\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2699 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.1499 Acc: 0.7442\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1847 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.9301 Acc: 0.7949\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1485 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.9835 Acc: 0.8007\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1885 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.9438 Acc: 0.7892\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1909 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.9743 Acc: 0.7892\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2183 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 1.0180 Acc: 0.7857\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1823 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 1.0282 Acc: 0.8122\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2046 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 1.0204 Acc: 0.8168\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2069 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 1.0498 Acc: 0.7719\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1651 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.1053 Acc: 0.7800\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1425 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 1.0906 Acc: 0.7650\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1835 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.1069 Acc: 0.7823\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1579 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 1.1559 Acc: 0.8168\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2043 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0028 Acc: 0.7880\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2430 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 1.1103 Acc: 0.7258\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1475 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 1.4332 Acc: 0.7247\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1322 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 1.0628 Acc: 0.8145\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2139 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.9909 Acc: 0.7834\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1893 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.1424 Acc: 0.7592\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2010 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.0831 Acc: 0.7535\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1487 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.9912 Acc: 0.7684\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1874 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.5003 Acc: 0.7419\n",
            "\n",
            "Training complete in 2m 58s\n",
            "Best val Acc: 0.816820\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1906 Acc: 0.9342\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0204 Acc: 0.8168\n",
            "\n",
            "Tested in 0m 4s Loss: 1.6408 Acc: 0.7227\n",
            "Accuracy. Train: 93.42% val: 81.68% test: 72.27%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.9229 Acc: 0.5074\n",
            "Phase val\n",
            "val Loss: 1.4715 Acc: 0.4505\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6781 Acc: 0.7342\n",
            "Phase val\n",
            "val Loss: 0.6889 Acc: 0.7823\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5302 Acc: 0.8107\n",
            "Phase val\n",
            "val Loss: 0.6000 Acc: 0.7765\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4418 Acc: 0.8443\n",
            "Phase val\n",
            "val Loss: 0.7050 Acc: 0.7454\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3554 Acc: 0.8604\n",
            "Phase val\n",
            "val Loss: 0.8326 Acc: 0.7362\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3330 Acc: 0.8926\n",
            "Phase val\n",
            "val Loss: 0.8577 Acc: 0.7408\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3708 Acc: 0.8671\n",
            "Phase val\n",
            "val Loss: 0.8715 Acc: 0.7811\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3203 Acc: 0.8832\n",
            "Phase val\n",
            "val Loss: 0.8738 Acc: 0.7258\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3165 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.8839 Acc: 0.7719\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2968 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 0.8146 Acc: 0.7558\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2662 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 1.2724 Acc: 0.7154\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2868 Acc: 0.9007\n",
            "Phase val\n",
            "val Loss: 1.2277 Acc: 0.6866\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2353 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.3676 Acc: 0.7546\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2403 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.1933 Acc: 0.7673\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1777 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 1.1896 Acc: 0.7419\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2190 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.9250 Acc: 0.7823\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2608 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 0.9120 Acc: 0.7696\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2418 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.9366 Acc: 0.7350\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1679 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.1258 Acc: 0.7707\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1917 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.2437 Acc: 0.7846\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1557 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 1.1195 Acc: 0.7512\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1536 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 1.2742 Acc: 0.8041\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2402 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0468 Acc: 0.7869\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1798 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 1.1627 Acc: 0.7373\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1502 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 1.2986 Acc: 0.7984\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1366 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 1.4079 Acc: 0.7627\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1498 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 1.2670 Acc: 0.7696\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1775 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.4863 Acc: 0.8191\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1753 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.9988 Acc: 0.8018\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1583 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 1.4678 Acc: 0.7869\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2000 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.2502 Acc: 0.7869\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1680 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 1.0903 Acc: 0.8111\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1663 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 1.0315 Acc: 0.8065\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1795 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.4183 Acc: 0.7431\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1871 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.4797 Acc: 0.6959\n",
            "\n",
            "Training complete in 2m 58s\n",
            "Best val Acc: 0.819124\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2009 Acc: 0.9302\n",
            "\n",
            "Tested in 0m 2s Loss: 1.4863 Acc: 0.8191\n",
            "\n",
            "Tested in 0m 4s Loss: 2.1676 Acc: 0.7429\n",
            "Accuracy. Train: 93.02% val: 81.91% test: 74.29%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 2.7203 Acc: 0.3879\n",
            "Phase val\n",
            "val Loss: 0.8029 Acc: 0.7408\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8113 Acc: 0.7007\n",
            "Phase val\n",
            "val Loss: 0.7248 Acc: 0.7915\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5381 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 0.7214 Acc: 0.7546\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4380 Acc: 0.8483\n",
            "Phase val\n",
            "val Loss: 0.9243 Acc: 0.7028\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4766 Acc: 0.8255\n",
            "Phase val\n",
            "val Loss: 1.0115 Acc: 0.7339\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3664 Acc: 0.8591\n",
            "Phase val\n",
            "val Loss: 0.6762 Acc: 0.7696\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2961 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.8473 Acc: 0.7776\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3069 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 1.0880 Acc: 0.7258\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2614 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 0.9412 Acc: 0.7419\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2461 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 1.2302 Acc: 0.7408\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2363 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.9652 Acc: 0.7857\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3007 Acc: 0.8953\n",
            "Phase val\n",
            "val Loss: 0.7265 Acc: 0.7915\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2234 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.8377 Acc: 0.8018\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2686 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 0.7937 Acc: 0.7661\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2369 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 0.8454 Acc: 0.7742\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2171 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.0151 Acc: 0.7684\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2161 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.8760 Acc: 0.7949\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1990 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 1.3018 Acc: 0.7707\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2034 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.9668 Acc: 0.7961\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1679 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.8573 Acc: 0.7707\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1645 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.8773 Acc: 0.8065\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1557 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.9029 Acc: 0.7846\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1633 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.1380 Acc: 0.7512\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1969 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0678 Acc: 0.7661\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1927 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.9380 Acc: 0.7938\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1601 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.0438 Acc: 0.7823\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1347 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.9174 Acc: 0.7995\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2068 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.0139 Acc: 0.8041\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1336 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 1.0633 Acc: 0.7800\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1835 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.0113 Acc: 0.7834\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1541 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.9342 Acc: 0.7938\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1206 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 1.1535 Acc: 0.7846\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1508 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 1.1666 Acc: 0.7615\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1666 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 1.3822 Acc: 0.7535\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1548 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 1.2415 Acc: 0.7961\n",
            "\n",
            "Training complete in 3m 1s\n",
            "Best val Acc: 0.806452\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1336 Acc: 0.9530\n",
            "\n",
            "Tested in 0m 2s Loss: 0.8773 Acc: 0.8065\n",
            "\n",
            "Tested in 0m 4s Loss: 1.1752 Acc: 0.7297\n",
            "Accuracy. Train: 95.30% val: 80.65% test: 72.97%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.9266 Acc: 0.5235\n",
            "Phase val\n",
            "val Loss: 0.6919 Acc: 0.7408\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7141 Acc: 0.7221\n",
            "Phase val\n",
            "val Loss: 1.0555 Acc: 0.6763\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5524 Acc: 0.7919\n",
            "Phase val\n",
            "val Loss: 0.6316 Acc: 0.7581\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4221 Acc: 0.8510\n",
            "Phase val\n",
            "val Loss: 0.6254 Acc: 0.8053\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3429 Acc: 0.8685\n",
            "Phase val\n",
            "val Loss: 0.7198 Acc: 0.7800\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4105 Acc: 0.8698\n",
            "Phase val\n",
            "val Loss: 0.8290 Acc: 0.7615\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2857 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 0.7531 Acc: 0.7915\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2550 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 1.1861 Acc: 0.7327\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3331 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 0.8051 Acc: 0.7995\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2612 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 0.7817 Acc: 0.7915\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2221 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 1.0103 Acc: 0.7546\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2295 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.9674 Acc: 0.7753\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2772 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 0.7951 Acc: 0.8203\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2226 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 0.9166 Acc: 0.7707\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1353 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 1.1563 Acc: 0.7880\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1721 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 1.0086 Acc: 0.7915\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2318 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.0010 Acc: 0.7373\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2499 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 1.1060 Acc: 0.7062\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1924 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.0309 Acc: 0.7719\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1768 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.1356 Acc: 0.7800\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1536 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 1.5070 Acc: 0.7327\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2038 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0848 Acc: 0.7719\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2075 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.9651 Acc: 0.7800\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1848 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0587 Acc: 0.7477\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1542 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 1.0669 Acc: 0.7638\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2531 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.0533 Acc: 0.7442\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1600 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 1.0642 Acc: 0.7512\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1714 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.1791 Acc: 0.7915\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1624 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 1.1501 Acc: 0.7834\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2039 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 1.2324 Acc: 0.7915\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1906 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 1.0988 Acc: 0.7719\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1472 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 1.0688 Acc: 0.8134\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1382 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 1.0691 Acc: 0.7984\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2105 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 1.3349 Acc: 0.7926\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1841 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.0888 Acc: 0.7535\n",
            "\n",
            "Training complete in 2m 58s\n",
            "Best val Acc: 0.820276\n",
            "\n",
            "Tested in 0m 2s Loss: 0.1960 Acc: 0.9356\n",
            "\n",
            "Tested in 0m 2s Loss: 0.7951 Acc: 0.8203\n",
            "\n",
            "Tested in 0m 4s Loss: 1.1861 Acc: 0.7303\n",
            "Accuracy. Train: 93.56% val: 82.03% test: 73.03%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 2.7513 Acc: 0.4027\n",
            "Phase val\n",
            "val Loss: 2.6281 Acc: 0.4240\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9713 Acc: 0.6497\n",
            "Phase val\n",
            "val Loss: 0.7638 Acc: 0.7592\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5914 Acc: 0.7624\n",
            "Phase val\n",
            "val Loss: 0.9521 Acc: 0.6855\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4937 Acc: 0.8228\n",
            "Phase val\n",
            "val Loss: 0.9546 Acc: 0.7258\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4332 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.7613 Acc: 0.7442\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3570 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.7843 Acc: 0.7581\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3292 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.6723 Acc: 0.7742\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2474 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.8527 Acc: 0.7661\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2902 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 0.7522 Acc: 0.8099\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2422 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.8682 Acc: 0.7673\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2434 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 0.7878 Acc: 0.7915\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2158 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.9259 Acc: 0.7926\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2318 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 0.8583 Acc: 0.8145\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1822 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.8730 Acc: 0.7972\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2236 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.8647 Acc: 0.7949\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2600 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 0.8405 Acc: 0.7753\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2064 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.9664 Acc: 0.7846\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1887 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.9773 Acc: 0.7558\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2140 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.0096 Acc: 0.7512\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1981 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.9087 Acc: 0.7846\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1817 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.9134 Acc: 0.8030\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1301 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 1.1994 Acc: 0.7869\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2336 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.3999 Acc: 0.7477\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1799 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 1.1162 Acc: 0.8007\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1789 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 1.2560 Acc: 0.7477\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1867 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.9864 Acc: 0.7650\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1510 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 1.0429 Acc: 0.7661\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1702 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.0242 Acc: 0.7742\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2134 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.9322 Acc: 0.7742\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1344 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 1.2574 Acc: 0.8180\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1322 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 1.1673 Acc: 0.8007\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1374 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 1.3055 Acc: 0.7684\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1988 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.6629 Acc: 0.7477\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1867 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 1.3328 Acc: 0.7281\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1819 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 1.2267 Acc: 0.7776\n",
            "\n",
            "Training complete in 2m 58s\n",
            "Best val Acc: 0.817972\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1391 Acc: 0.9530\n",
            "\n",
            "Tested in 0m 2s Loss: 1.2574 Acc: 0.8180\n",
            "\n",
            "Tested in 0m 4s Loss: 1.6157 Acc: 0.7631\n",
            "Accuracy. Train: 95.30% val: 81.80% test: 76.31%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xvVpuJUC-Yj",
        "colab_type": "code",
        "outputId": "a225a28c-8b2c-46f6-d566-9010e76cac3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB-D with fine tuned layers accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB-D with fine tuned layers accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB-D with fine tuned layers accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB-D with fine tuned layers accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB-D with fine tuned layers accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB-D with fine tuned layers accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB-D with fine tuned layers accuracy 94.12080645561218%\n",
            "StDev training RGB-D with fine tuned layers accuracy 0.9805090725421906%\n",
            "Mean validation RGB-D with fine tuned layers accuracy 81.61290287971497%\n",
            "StDev validation RGB-D with fine tuned layers accuracy 0.49739666283130646%\n",
            "Mean test RGB-D with fine tuned layers accuracy 73.7744152545929%\n",
            "StDev test RGB-D with fine tuned layers accuracy 1.4237914234399796%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awIzIjrMo1h7",
        "colab_type": "text"
      },
      "source": [
        "With some fine tuning of layers we reached no improvement with respect to RGBD training, and we needed to add 10 more epochs to the training because we observed that the training was slower. Maybe playing with a different learning rate scheduler could help to address this.\n",
        "\n",
        "Experiment Name | Train Accuracy (Avg/StdDev)| Val Accuracy (Avg/StdDev)| Test accuracy (Avg/StdDev)|\n",
        "--- | --- | --- | ---\n",
        "RGB-D | 91.92 % / 3.64 % | 80.94% / 0.58 % | 75.79 % / 0.68 %\n",
        "RGB-D-finetuned | 94.12 % / 0.98 % | 81.61% / 0.50 % | 73.77 % / 1.43 %"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-v-tj1p0gkv",
        "colab_type": "text"
      },
      "source": [
        "## AlexNet pretrained on Places365\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iA1SGlDvb8C9",
        "colab_type": "text"
      },
      "source": [
        "### Load new AlexNet pretrained weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3B3jHQME8A7a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load the pre-trained weights\n",
        "branch_model_file = '/content/gdrive/My Drive/MultiModal/models/alexnet_places365.pth.tar'\n",
        "rgb_branch = models.__dict__['alexnet'](num_classes=365)\n",
        "hha_branch = models.__dict__['alexnet'](num_classes=365)\n",
        "\n",
        "checkpoint = torch.load(branch_model_file, map_location=lambda storage, loc: storage)\n",
        "state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}\n",
        "rgb_branch.load_state_dict(state_dict)\n",
        "rgb_branch.eval()\n",
        "hha_branch.load_state_dict(state_dict)\n",
        "hha_branch.eval()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqgGp9xI0f6v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Instantiate the model\n",
        "def initialize_model_alexnet_places365():\n",
        "  branch_model_file = '/content/gdrive/My Drive/MultiModal/models/alexnet_places365.pth.tar'\n",
        "  rgb_branch = models.__dict__['alexnet'](num_classes=365)\n",
        "  hha_branch = models.__dict__['alexnet'](num_classes=365)\n",
        "\n",
        "  checkpoint = torch.load(branch_model_file, map_location=lambda storage, loc: storage)\n",
        "  state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}\n",
        "  rgb_branch.load_state_dict(state_dict)\n",
        "  rgb_branch.eval()\n",
        "  hha_branch.load_state_dict(state_dict)\n",
        "  hha_branch.eval()\n",
        "  num_classes = len(class_names)\n",
        "  model = RGBDnet(rgb_branch=rgb_branch,\n",
        "                  hha_branch=hha_branch, \n",
        "                  num_classes=num_classes)\n",
        "\n",
        "  # Here we define the learning rate\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "    \n",
        "  for param in model.classifier.parameters():\n",
        "    param.requires_grad = True\n",
        "    \n",
        "  learning_rate = 0.001\n",
        "    \n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "  return model, criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Llulp648cKJ1",
        "colab_type": "text"
      },
      "source": [
        "### Train And evaluate using AlexNet on Places365 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fPT-VBc9OMm",
        "colab_type": "code",
        "outputId": "ade56b43-c20f-4c72-e5a6-43bb6a827df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMultiModal(multimodal_dataloaders, initialize_model_alexnet_places365), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9987 Acc: 0.6336\n",
            "Phase val\n",
            "val Loss: 0.6023 Acc: 0.7938\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5257 Acc: 0.8067\n",
            "Phase val\n",
            "val Loss: 0.5958 Acc: 0.7984\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4463 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.6023 Acc: 0.7903\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3651 Acc: 0.8698\n",
            "Phase val\n",
            "val Loss: 0.5823 Acc: 0.8007\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3571 Acc: 0.8752\n",
            "Phase val\n",
            "val Loss: 0.5715 Acc: 0.7972\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3042 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.6121 Acc: 0.7984\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2691 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.6221 Acc: 0.7661\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2609 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.6584 Acc: 0.7592\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2606 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 0.6141 Acc: 0.7926\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2415 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.6409 Acc: 0.7800\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2087 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6384 Acc: 0.7661\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2187 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.6647 Acc: 0.7846\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2422 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.6353 Acc: 0.7776\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2096 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6972 Acc: 0.7650\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2053 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6665 Acc: 0.7880\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1921 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6799 Acc: 0.7915\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2073 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6604 Acc: 0.7834\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1405 Acc: 0.9678\n",
            "Phase val\n",
            "val Loss: 0.6954 Acc: 0.7765\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1634 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.7132 Acc: 0.7673\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1549 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7257 Acc: 0.7696\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1823 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.7048 Acc: 0.7742\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1890 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.7008 Acc: 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1504 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7158 Acc: 0.7765\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1704 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.7178 Acc: 0.7811\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1598 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.7805 Acc: 0.7788\n",
            "\n",
            "Training complete in 2m 20s\n",
            "Best val Acc: 0.800691\n",
            "\n",
            "Tested in 0m 3s Loss: 0.3586 Acc: 0.8671\n",
            "\n",
            "Tested in 0m 3s Loss: 0.5823 Acc: 0.8007\n",
            "\n",
            "Tested in 0m 5s Loss: 0.6873 Acc: 0.7505\n",
            "Accuracy. Train: 86.71% val: 80.07% test: 75.05%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.0098 Acc: 0.6309\n",
            "Phase val\n",
            "val Loss: 0.6240 Acc: 0.7707\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5872 Acc: 0.7812\n",
            "Phase val\n",
            "val Loss: 0.7049 Acc: 0.7396\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4561 Acc: 0.8255\n",
            "Phase val\n",
            "val Loss: 0.6125 Acc: 0.7926\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3781 Acc: 0.8537\n",
            "Phase val\n",
            "val Loss: 0.5671 Acc: 0.8053\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3314 Acc: 0.8685\n",
            "Phase val\n",
            "val Loss: 0.6290 Acc: 0.7650\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3036 Acc: 0.8926\n",
            "Phase val\n",
            "val Loss: 0.6132 Acc: 0.7949\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2999 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 0.5924 Acc: 0.7949\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2392 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 0.6026 Acc: 0.8007\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2798 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.6926 Acc: 0.7765\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2376 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 0.6168 Acc: 0.7926\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2187 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.5987 Acc: 0.8041\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2217 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 0.7012 Acc: 0.7558\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2095 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6273 Acc: 0.7961\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2018 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6768 Acc: 0.7730\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1981 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6366 Acc: 0.8041\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2165 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6609 Acc: 0.8007\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1929 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6718 Acc: 0.7834\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1882 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6794 Acc: 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1772 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6644 Acc: 0.7892\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1622 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6402 Acc: 0.8018\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1631 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.7292 Acc: 0.7800\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1656 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.6895 Acc: 0.7880\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1351 Acc: 0.9705\n",
            "Phase val\n",
            "val Loss: 0.7157 Acc: 0.7857\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1816 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.7063 Acc: 0.7800\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1632 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6992 Acc: 0.7846\n",
            "\n",
            "Training complete in 2m 18s\n",
            "Best val Acc: 0.805300\n",
            "\n",
            "Tested in 0m 3s Loss: 0.3222 Acc: 0.8765\n",
            "\n",
            "Tested in 0m 3s Loss: 0.5671 Acc: 0.8053\n",
            "\n",
            "Tested in 0m 5s Loss: 0.6739 Acc: 0.7643\n",
            "Accuracy. Train: 87.65% val: 80.53% test: 76.43%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.0398 Acc: 0.6067\n",
            "Phase val\n",
            "val Loss: 0.6757 Acc: 0.7627\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5302 Acc: 0.8013\n",
            "Phase val\n",
            "val Loss: 0.6092 Acc: 0.7800\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4307 Acc: 0.8376\n",
            "Phase val\n",
            "val Loss: 0.5839 Acc: 0.7834\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3705 Acc: 0.8577\n",
            "Phase val\n",
            "val Loss: 0.6001 Acc: 0.7684\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3398 Acc: 0.8819\n",
            "Phase val\n",
            "val Loss: 0.5858 Acc: 0.7915\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2874 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 0.6027 Acc: 0.7949\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2890 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.5696 Acc: 0.8007\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2638 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.6568 Acc: 0.7707\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2471 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 0.6272 Acc: 0.7834\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2319 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6504 Acc: 0.7719\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2089 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6227 Acc: 0.7903\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2004 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6181 Acc: 0.7915\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1955 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6129 Acc: 0.8099\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2077 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6998 Acc: 0.7558\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2032 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.6271 Acc: 0.8018\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1734 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6835 Acc: 0.7776\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1733 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6390 Acc: 0.7949\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1758 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6347 Acc: 0.7972\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1689 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.6397 Acc: 0.7903\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1782 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.7239 Acc: 0.7834\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1372 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6555 Acc: 0.7984\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1581 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.7405 Acc: 0.7650\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1624 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6887 Acc: 0.7926\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1499 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.7210 Acc: 0.7753\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1365 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.7597 Acc: 0.7661\n",
            "\n",
            "Training complete in 2m 18s\n",
            "Best val Acc: 0.809908\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1979 Acc: 0.9396\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6129 Acc: 0.8099\n",
            "\n",
            "Tested in 0m 5s Loss: 0.7579 Acc: 0.7757\n",
            "Accuracy. Train: 93.96% val: 80.99% test: 77.57%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9728 Acc: 0.6631\n",
            "Phase val\n",
            "val Loss: 0.6510 Acc: 0.8065\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5424 Acc: 0.8000\n",
            "Phase val\n",
            "val Loss: 0.5785 Acc: 0.7949\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4034 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.5545 Acc: 0.7961\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3751 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 0.5812 Acc: 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3520 Acc: 0.8698\n",
            "Phase val\n",
            "val Loss: 0.5795 Acc: 0.7972\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3149 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 0.5994 Acc: 0.7903\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2697 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 0.6195 Acc: 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2541 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.5965 Acc: 0.8065\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2573 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.5970 Acc: 0.7984\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2416 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.6511 Acc: 0.7961\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2306 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 0.6878 Acc: 0.7800\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2238 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.6386 Acc: 0.7995\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1972 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6713 Acc: 0.7915\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2224 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6391 Acc: 0.7961\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1616 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.6672 Acc: 0.7949\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1900 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6767 Acc: 0.7800\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1936 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6647 Acc: 0.7938\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1878 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.7305 Acc: 0.7800\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1724 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.7047 Acc: 0.7926\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1762 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.6851 Acc: 0.7776\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1813 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.7846 Acc: 0.7546\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1895 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6860 Acc: 0.7995\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1725 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.7537 Acc: 0.7696\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1353 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7276 Acc: 0.7869\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1350 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.7156 Acc: 0.7926\n",
            "\n",
            "Training complete in 2m 18s\n",
            "Best val Acc: 0.806452\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6045 Acc: 0.7839\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6510 Acc: 0.8065\n",
            "\n",
            "Tested in 0m 5s Loss: 0.7685 Acc: 0.7473\n",
            "Accuracy. Train: 78.39% val: 80.65% test: 74.73%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9438 Acc: 0.6671\n",
            "Phase val\n",
            "val Loss: 0.5818 Acc: 0.7903\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5203 Acc: 0.8013\n",
            "Phase val\n",
            "val Loss: 0.6133 Acc: 0.7880\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3908 Acc: 0.8497\n",
            "Phase val\n",
            "val Loss: 0.6040 Acc: 0.7765\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3591 Acc: 0.8577\n",
            "Phase val\n",
            "val Loss: 0.5953 Acc: 0.7903\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3105 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.6093 Acc: 0.7730\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3108 Acc: 0.8953\n",
            "Phase val\n",
            "val Loss: 0.5969 Acc: 0.7961\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2576 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 0.6781 Acc: 0.7650\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2932 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 0.6558 Acc: 0.7765\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2329 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.5965 Acc: 0.8018\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2260 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.6649 Acc: 0.7638\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2381 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 0.6438 Acc: 0.7846\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2235 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6383 Acc: 0.7949\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2017 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.6360 Acc: 0.7869\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2037 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6524 Acc: 0.7788\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1868 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.6761 Acc: 0.7753\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1885 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.6435 Acc: 0.8099\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1924 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6668 Acc: 0.8007\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1649 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6878 Acc: 0.7834\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1805 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.7074 Acc: 0.7776\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1898 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.7163 Acc: 0.7846\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1640 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6958 Acc: 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1515 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.7010 Acc: 0.7765\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1686 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.7746 Acc: 0.7650\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1374 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6570 Acc: 0.8088\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1581 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6825 Acc: 0.7834\n",
            "\n",
            "Training complete in 2m 19s\n",
            "Best val Acc: 0.809908\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1885 Acc: 0.9436\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6435 Acc: 0.8099\n",
            "\n",
            "Tested in 0m 5s Loss: 0.7612 Acc: 0.7662\n",
            "Accuracy. Train: 94.36% val: 80.99% test: 76.62%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNLH_5lo9OtE",
        "colab_type": "code",
        "outputId": "b02959f9-955a-46cd-de0a-612d8ff7ef57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB-D with AlexNet Places365 accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB-D with AlexNet Places365 accuracy 87.94631958007812%\n",
            "StDev training RGB-D with AlexNet Places365 accuracy 3.3523738384246826%\n",
            "Mean validation RGB-D with AlexNet Places365 accuracy 80.92166185379028%\n",
            "StDev validation RGB-D with AlexNet Places365 accuracy 0.2481653355062008%\n",
            "Mean test RGB-D with AlexNet Places365 accuracy 76.42091512680054%\n",
            "StDev test RGB-D with AlexNet Places365 accuracy 0.9160803630948067%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-g_LPnAUssk6",
        "colab_type": "text"
      },
      "source": [
        "Opposite to what was expected, could not manage to see a boost in performance when using AlexNet trained on Places365"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFLbmWSmLCmC",
        "colab_type": "text"
      },
      "source": [
        "## Fusion at different layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJeHeozX1Q_j",
        "colab_type": "text"
      },
      "source": [
        "We try to build an architecture that does not only concatenate features coming from different modalities at the last stage of the network, but that also merges outputs from inner layers.\n",
        "\n",
        "Our idea is based on having a 'fusion branch' that has 2 convolutional layers in its convolutional part and a couple fully connected layers (kind of mimicking the AlexNet architecture).\n",
        "\n",
        "The first of the convolutional layers will get the output feature maps of the penultimate convolutionals of both RGB and HHA branches. (That would mean input of 512 feature maps) (This corresponds to the layer idx 10 of the features part of AlexNet)\n",
        "\n",
        "After the firts conv layer, the last feature maps of the RGB and HHA layers are reinjected again. (Thus having 512 + 256) input channels at that stage.\n",
        "\n",
        "At the last step in the classifiers, the feature vectors of the three branches is concatenated again to be connected to the output classification layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaFgHwvcbtto",
        "colab_type": "text"
      },
      "source": [
        "###Fusion network architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WccIuxsLNyg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FIRST_CONV_LAYER_IDX_OUT = 10\n",
        "\n",
        "# In PyTorch every network is implementd as a nn.Module\n",
        "class RGBDFusionNet(nn.Module):\n",
        "    # The parameters are initialized in __init__(self, ...)\n",
        "    def __init__(self, rgb_branch, hha_branch, num_classes):\n",
        "        super(RGBDFusionNet, self).__init__()\n",
        "        \n",
        "        # RGB branch\n",
        "        model_rgb = rgb_branch\n",
        "        self.rgb_convs = model_rgb.features\n",
        "        c = model_rgb.classifier\n",
        "        self.rgb_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        num_ftrs_rgb = c[4].out_features\n",
        "\n",
        "        # HHA branch\n",
        "        model_hha = hha_branch\n",
        "        self.hha_convs = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        self.hha_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        f = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        num_ftrs_hha = c[4].out_features\n",
        "        \n",
        "        #Branch to fusion features from the RGB and HHA branch\n",
        "        self.fusion_features = nn.Sequential(\n",
        "            #First convolution of features will receive as input the concatenated feature maps\n",
        "            #from the output of the penultimate convolutional layers of both RGB and HHA branches\n",
        "            nn.Conv2d(512, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "            #Second convolution of features will receive as input the concatenated feature maps\n",
        "            #from the output of the last convolutional layers of both RGB and HHA branches plus the result\n",
        "            #of the convolution of its previous layer\n",
        "            nn.Conv2d(512 + 256, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "        self.fusion_fcs = nn.Sequential(\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(256 * 6 * 6, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, 4096)\n",
        "        )\n",
        "\n",
        "        self.rgb_avgpool = rgb_branch.avgpool\n",
        "        self.hha_avgpool = rgb_branch.avgpool\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\n",
        "        num_ftrs_fusion = 4096\n",
        "\n",
        "        self.classifier = nn.Linear(num_ftrs_rgb+num_ftrs_hha+num_ftrs_fusion, num_classes)\n",
        "\n",
        "    def forward_branch_conv(self, branch_convs, first_layer, last_layer, x):\n",
        "      for layer in branch_convs[first_layer:last_layer]:\n",
        "        x = layer(x)\n",
        "      return x\n",
        "\n",
        "    # The data flow is defined in forward. No need to specify backward operations (PyTorch takes care of them)\n",
        "    def forward(self, x):\n",
        "      x_rgb = x[0]\n",
        "      #Extract first feature maps to inject to fusion branch from RGB\n",
        "      x_rgb = self.forward_branch_conv(self.rgb_convs, 0, FIRST_CONV_LAYER_IDX_OUT, x_rgb)\n",
        "      \n",
        "      x_hha = x[1]\n",
        "      #Extract first feature maps to inject to fusion branch from HHA\n",
        "      x_hha = self.forward_branch_conv(self.hha_convs, 0, FIRST_CONV_LAYER_IDX_OUT, x_hha)\n",
        "      \n",
        "      #Concatenate first extracted feature maps and pass them as input to fusion_features branch\n",
        "      x_fusion = torch.cat((x_rgb, x_hha), 1)\n",
        "      x_fusion = self.forward_branch_conv(self.fusion_features, 0, 3, x_fusion)\n",
        "\n",
        "      #Extract second feature maps to inject to fusion branch from RGB\n",
        "      x_rgb = self.forward_branch_conv(self.rgb_convs, 10, len(self.rgb_convs), x_rgb)\n",
        "      #Extract second feature maps to inject to fusion branch from HHA\n",
        "      x_hha = self.forward_branch_conv(self.hha_convs, 10, len(self.hha_convs), x_hha)\n",
        "\n",
        "      #Reinject to fusion_features the second extracted feature maps from the branches\n",
        "      x_fusion = torch.cat((x_fusion, torch.cat((x_rgb, x_hha), 1)), 1)\n",
        "      x_fusion = self.forward_branch_conv(self.fusion_features, 3, len(self.fusion_features), x_fusion)\n",
        "      x_fusion = self.avgpool(x_fusion)\n",
        "      x_fusion = torch.flatten(x_fusion, 1)\n",
        "      x_rgb = self.rgb_avgpool(x_rgb)\n",
        "      x_rgb = torch.flatten(x_rgb, 1)\n",
        "      x_hha = self.rgb_avgpool(x_hha)\n",
        "      x_hha = torch.flatten(x_hha, 1)\n",
        "      #Pass three branches to their respectives Fully Connected parts\n",
        "      x_rgb = self.rgb_fcs(x_rgb)\n",
        "      x_hha = self.hha_fcs(x_hha)\n",
        "      x_fusion = self.fusion_fcs(x_fusion)\n",
        "\n",
        "      #Join the 3 feature vectors to pass to classifier\n",
        "      x = torch.cat((x_rgb, x_hha), 1)\n",
        "      x = torch.cat((x, x_fusion), 1)\n",
        "      x = self.classifier(x)\n",
        "      return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJAfOntvxgXS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision.models\n",
        "\n",
        "def initialize_model_fusion():\n",
        "  # Instantiate the model\n",
        "  num_classes = len(class_names)\n",
        "  model = RGBDFusionNet(rgb_branch=models.alexnet(pretrained=True),\n",
        "                      hha_branch=models.alexnet(pretrained=True), \n",
        "                      num_classes=num_classes)\n",
        "\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate = 0.001\n",
        "\n",
        "  for param in model.fusion_features.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.fusion_fcs.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.classifier.parameters():\n",
        "    param.requires_grad = True\n",
        "    \n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "  return model, criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fano98hFb0zu",
        "colab_type": "text"
      },
      "source": [
        "### Train And evaluate Fusion at different layers 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-W5iD742lCw",
        "colab_type": "code",
        "outputId": "88487d27-0384-4c53-8a4f-7c6aef1a55ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMultiModal(multimodal_dataloaders, initialize_model_fusion, num_epochs=35), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 9.8749 Acc: 0.5436\n",
            "Phase val\n",
            "val Loss: 0.7480 Acc: 0.6959\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5653 Acc: 0.8013\n",
            "Phase val\n",
            "val Loss: 0.6699 Acc: 0.7730\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4500 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.5823 Acc: 0.7938\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3450 Acc: 0.8671\n",
            "Phase val\n",
            "val Loss: 0.5737 Acc: 0.7926\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3160 Acc: 0.8940\n",
            "Phase val\n",
            "val Loss: 0.5950 Acc: 0.7926\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2793 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 0.5675 Acc: 0.7984\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2383 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.5983 Acc: 0.7926\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2219 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.6046 Acc: 0.8088\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2442 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.6163 Acc: 0.7869\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2439 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.7014 Acc: 0.7465\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2105 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.5570 Acc: 0.8041\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2021 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.5869 Acc: 0.7949\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1862 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6525 Acc: 0.7638\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1815 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.5595 Acc: 0.8030\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1595 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6511 Acc: 0.7972\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1607 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6540 Acc: 0.7880\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1937 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6659 Acc: 0.7604\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1692 Acc: 0.9450\n",
            "Phase val\n",
            "val Loss: 0.6810 Acc: 0.7811\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1651 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.6797 Acc: 0.7984\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1599 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6500 Acc: 0.7753\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1816 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6884 Acc: 0.7673\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1803 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6265 Acc: 0.7949\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1447 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.7577 Acc: 0.7696\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1289 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6474 Acc: 0.7915\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1110 Acc: 0.9678\n",
            "Phase val\n",
            "val Loss: 0.6749 Acc: 0.7753\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1267 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6767 Acc: 0.7938\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1359 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.6163 Acc: 0.7984\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1139 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.6765 Acc: 0.7880\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1200 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.7166 Acc: 0.7684\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1076 Acc: 0.9678\n",
            "Phase val\n",
            "val Loss: 0.7102 Acc: 0.7892\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1192 Acc: 0.9678\n",
            "Phase val\n",
            "val Loss: 0.6732 Acc: 0.7811\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1303 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6944 Acc: 0.7892\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1122 Acc: 0.9705\n",
            "Phase val\n",
            "val Loss: 0.6444 Acc: 0.7903\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1153 Acc: 0.9638\n",
            "Phase val\n",
            "val Loss: 0.7377 Acc: 0.7880\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1150 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.6718 Acc: 0.7650\n",
            "\n",
            "Training complete in 3m 19s\n",
            "Best val Acc: 0.808756\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2436 Acc: 0.9181\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6046 Acc: 0.8088\n",
            "\n",
            "Tested in 0m 5s Loss: 0.7823 Acc: 0.7681\n",
            "Accuracy. Train: 91.81% val: 80.88% test: 76.81%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 7.9118 Acc: 0.5087\n",
            "Phase val\n",
            "val Loss: 0.9562 Acc: 0.6509\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6226 Acc: 0.7557\n",
            "Phase val\n",
            "val Loss: 0.6767 Acc: 0.7512\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4773 Acc: 0.8215\n",
            "Phase val\n",
            "val Loss: 0.6043 Acc: 0.7834\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3457 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.7248 Acc: 0.7558\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4039 Acc: 0.8644\n",
            "Phase val\n",
            "val Loss: 0.5619 Acc: 0.8030\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3210 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 0.5942 Acc: 0.7972\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2576 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 0.6248 Acc: 0.7834\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2539 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 0.6111 Acc: 0.7800\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2387 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 0.5890 Acc: 0.8145\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2484 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 0.5602 Acc: 0.8041\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2312 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.6261 Acc: 0.7926\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2041 Acc: 0.9275\n",
            "Phase val\n",
            "val Loss: 0.6239 Acc: 0.7903\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2323 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6027 Acc: 0.8076\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2117 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6155 Acc: 0.7915\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1899 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6009 Acc: 0.7949\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1684 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.6272 Acc: 0.8053\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2016 Acc: 0.9369\n",
            "Phase val\n",
            "val Loss: 0.6812 Acc: 0.7742\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1673 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6874 Acc: 0.7846\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1626 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6927 Acc: 0.7926\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1660 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6409 Acc: 0.7880\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1621 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6871 Acc: 0.7984\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1552 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.6649 Acc: 0.7984\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1272 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.6990 Acc: 0.7776\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1508 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.6441 Acc: 0.7995\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1299 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.6384 Acc: 0.7880\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1415 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6636 Acc: 0.7972\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1323 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.6998 Acc: 0.7650\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1315 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6823 Acc: 0.7949\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1624 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.8506 Acc: 0.7604\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1766 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.7729 Acc: 0.7753\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1584 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.6561 Acc: 0.7811\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1198 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.7189 Acc: 0.7823\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1338 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.6435 Acc: 0.7857\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1166 Acc: 0.9611\n",
            "Phase val\n",
            "val Loss: 0.7452 Acc: 0.7707\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1272 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.7135 Acc: 0.7984\n",
            "\n",
            "Training complete in 3m 17s\n",
            "Best val Acc: 0.814516\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2166 Acc: 0.9329\n",
            "\n",
            "Tested in 0m 3s Loss: 0.5890 Acc: 0.8145\n",
            "\n",
            "Tested in 0m 5s Loss: 0.8128 Acc: 0.7517\n",
            "Accuracy. Train: 93.29% val: 81.45% test: 75.17%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 4.7981 Acc: 0.4886\n",
            "Phase val\n",
            "val Loss: 0.8063 Acc: 0.7200\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6570 Acc: 0.7490\n",
            "Phase val\n",
            "val Loss: 0.8016 Acc: 0.7247\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5126 Acc: 0.8054\n",
            "Phase val\n",
            "val Loss: 0.7324 Acc: 0.7523\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3937 Acc: 0.8523\n",
            "Phase val\n",
            "val Loss: 0.6524 Acc: 0.7488\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3183 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.6289 Acc: 0.7857\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3211 Acc: 0.8886\n",
            "Phase val\n",
            "val Loss: 0.5767 Acc: 0.8053\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3053 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.5685 Acc: 0.8088\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2551 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6054 Acc: 0.7811\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2361 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6169 Acc: 0.8191\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2146 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6049 Acc: 0.7961\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2105 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.6355 Acc: 0.7834\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2197 Acc: 0.9342\n",
            "Phase val\n",
            "val Loss: 0.6109 Acc: 0.8030\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2223 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6773 Acc: 0.8018\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2194 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.6835 Acc: 0.7661\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1620 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.6156 Acc: 0.7915\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1662 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6643 Acc: 0.7730\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1626 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6511 Acc: 0.7903\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1693 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6033 Acc: 0.8088\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1577 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.8675 Acc: 0.7408\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1413 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6313 Acc: 0.7869\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1542 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.7709 Acc: 0.7477\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1773 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6969 Acc: 0.8030\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1603 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.7577 Acc: 0.7523\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1672 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6995 Acc: 0.8088\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1700 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.7256 Acc: 0.8111\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1345 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7014 Acc: 0.7972\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1201 Acc: 0.9664\n",
            "Phase val\n",
            "val Loss: 0.6847 Acc: 0.8088\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1668 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.7303 Acc: 0.7546\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1440 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.7150 Acc: 0.7811\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1547 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.8196 Acc: 0.7500\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1313 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.7176 Acc: 0.7949\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1528 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.8700 Acc: 0.7512\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1327 Acc: 0.9638\n",
            "Phase val\n",
            "val Loss: 0.6823 Acc: 0.7788\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1491 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.7567 Acc: 0.7938\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1071 Acc: 0.9638\n",
            "Phase val\n",
            "val Loss: 0.7632 Acc: 0.7661\n",
            "\n",
            "Training complete in 3m 17s\n",
            "Best val Acc: 0.819124\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2846 Acc: 0.9047\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6169 Acc: 0.8191\n",
            "\n",
            "Tested in 0m 5s Loss: 0.8175 Acc: 0.7580\n",
            "Accuracy. Train: 90.47% val: 81.91% test: 75.80%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 6.3682 Acc: 0.4886\n",
            "Phase val\n",
            "val Loss: 0.8635 Acc: 0.6751\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6215 Acc: 0.7584\n",
            "Phase val\n",
            "val Loss: 0.6568 Acc: 0.7431\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4372 Acc: 0.8497\n",
            "Phase val\n",
            "val Loss: 0.5951 Acc: 0.7834\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3536 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.5642 Acc: 0.8041\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2949 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.5932 Acc: 0.7834\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3315 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.6034 Acc: 0.7915\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2692 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.5777 Acc: 0.7972\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2753 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.5946 Acc: 0.8007\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2433 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.7161 Acc: 0.7523\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2151 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6559 Acc: 0.7788\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2138 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 0.6539 Acc: 0.7869\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2133 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 0.6203 Acc: 0.7892\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1978 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6710 Acc: 0.7753\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1822 Acc: 0.9477\n",
            "Phase val\n",
            "val Loss: 0.6086 Acc: 0.8007\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1713 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.6324 Acc: 0.7788\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1763 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6870 Acc: 0.7800\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1920 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.6912 Acc: 0.7857\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1777 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 0.6203 Acc: 0.8076\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1684 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6478 Acc: 0.7869\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1712 Acc: 0.9463\n",
            "Phase val\n",
            "val Loss: 0.6621 Acc: 0.7788\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1708 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.7919 Acc: 0.7373\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1626 Acc: 0.9436\n",
            "Phase val\n",
            "val Loss: 0.6719 Acc: 0.7673\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1417 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6709 Acc: 0.7972\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1503 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.7024 Acc: 0.7696\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1392 Acc: 0.9503\n",
            "Phase val\n",
            "val Loss: 0.6727 Acc: 0.7915\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1197 Acc: 0.9691\n",
            "Phase val\n",
            "val Loss: 0.7008 Acc: 0.7742\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1339 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.7491 Acc: 0.7650\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1287 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.7448 Acc: 0.7696\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1501 Acc: 0.9490\n",
            "Phase val\n",
            "val Loss: 0.7128 Acc: 0.7869\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1120 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.6966 Acc: 0.7846\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1303 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.7130 Acc: 0.7869\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1120 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.7214 Acc: 0.7742\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1347 Acc: 0.9651\n",
            "Phase val\n",
            "val Loss: 0.6553 Acc: 0.7903\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1260 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.7002 Acc: 0.7880\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1122 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.7121 Acc: 0.7823\n",
            "\n",
            "Training complete in 3m 17s\n",
            "Best val Acc: 0.807604\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1548 Acc: 0.9490\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6203 Acc: 0.8076\n",
            "\n",
            "Tested in 0m 5s Loss: 0.8333 Acc: 0.7486\n",
            "Accuracy. Train: 94.90% val: 80.76% test: 74.86%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 10.7654 Acc: 0.5879\n",
            "Phase val\n",
            "val Loss: 0.6234 Acc: 0.7673\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5345 Acc: 0.8161\n",
            "Phase val\n",
            "val Loss: 0.6483 Acc: 0.7800\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4099 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.5819 Acc: 0.7972\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3648 Acc: 0.8644\n",
            "Phase val\n",
            "val Loss: 0.6127 Acc: 0.7823\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2953 Acc: 0.8859\n",
            "Phase val\n",
            "val Loss: 0.6017 Acc: 0.7730\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3023 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.5886 Acc: 0.8030\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2751 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.6111 Acc: 0.7926\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2611 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.6744 Acc: 0.7419\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2200 Acc: 0.9315\n",
            "Phase val\n",
            "val Loss: 0.5911 Acc: 0.8018\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2281 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 0.6384 Acc: 0.7846\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2307 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 0.6250 Acc: 0.7938\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2174 Acc: 0.9262\n",
            "Phase val\n",
            "val Loss: 0.6938 Acc: 0.7730\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2067 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.6096 Acc: 0.7811\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1869 Acc: 0.9396\n",
            "Phase val\n",
            "val Loss: 0.6859 Acc: 0.7673\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1860 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6454 Acc: 0.7834\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1943 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 0.6348 Acc: 0.7696\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1965 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 0.6301 Acc: 0.8214\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1719 Acc: 0.9423\n",
            "Phase val\n",
            "val Loss: 0.7482 Acc: 0.7512\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1433 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.6341 Acc: 0.8053\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1352 Acc: 0.9570\n",
            "Phase val\n",
            "val Loss: 0.6525 Acc: 0.7972\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1393 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.6635 Acc: 0.7903\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1357 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 0.6833 Acc: 0.7730\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1327 Acc: 0.9664\n",
            "Phase val\n",
            "val Loss: 0.6388 Acc: 0.8053\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1363 Acc: 0.9584\n",
            "Phase val\n",
            "val Loss: 0.6998 Acc: 0.7765\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1067 Acc: 0.9718\n",
            "Phase val\n",
            "val Loss: 0.6520 Acc: 0.8111\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1143 Acc: 0.9691\n",
            "Phase val\n",
            "val Loss: 0.7587 Acc: 0.7535\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1136 Acc: 0.9624\n",
            "Phase val\n",
            "val Loss: 0.6619 Acc: 0.7984\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1481 Acc: 0.9530\n",
            "Phase val\n",
            "val Loss: 0.6640 Acc: 0.7961\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1254 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.8469 Acc: 0.7558\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1316 Acc: 0.9517\n",
            "Phase val\n",
            "val Loss: 0.6494 Acc: 0.8053\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1455 Acc: 0.9597\n",
            "Phase val\n",
            "val Loss: 0.7892 Acc: 0.7500\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1169 Acc: 0.9718\n",
            "Phase val\n",
            "val Loss: 0.8675 Acc: 0.7488\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1388 Acc: 0.9557\n",
            "Phase val\n",
            "val Loss: 0.6913 Acc: 0.7972\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1051 Acc: 0.9651\n",
            "Phase val\n",
            "val Loss: 0.7536 Acc: 0.7546\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1270 Acc: 0.9544\n",
            "Phase val\n",
            "val Loss: 0.7803 Acc: 0.7880\n",
            "\n",
            "Training complete in 3m 18s\n",
            "Best val Acc: 0.821429\n",
            "\n",
            "Tested in 0m 3s Loss: 0.1756 Acc: 0.9383\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6301 Acc: 0.8214\n",
            "\n",
            "Tested in 0m 5s Loss: 0.8568 Acc: 0.7612\n",
            "Accuracy. Train: 93.83% val: 82.14% test: 76.12%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aofGrsLADXb8",
        "colab_type": "code",
        "outputId": "55b8aec2-b65f-4084-d489-c8d1bfea6e49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB-D with Fusion at different levels accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB-D with Fusion at different levels accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB-D with Fusion at different levels accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB-D with Fusion at different levels accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB-D with Fusion at different levels accuracy {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB-D with Fusion at different levels accuracy {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB-D with Fusion at different levels accuracy 92.85905957221985%\n",
            "StDev training RGB-D with Fusion at different levels accuracy 1.5547310933470726%\n",
            "Mean validation RGB-D with Fusion at different levels accuracy 81.42857551574707%\n",
            "StDev validation RGB-D with Fusion at different levels accuracy 0.5472057498991489%\n",
            "Mean test RGB-D with Fusion at different levels accuracy 75.75299143791199%\n",
            "StDev test RGB-D with Fusion at different levels accuracy 0.6918707862496376%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLNEbZ7q62Wm",
        "colab_type": "text"
      },
      "source": [
        "With fusioning output at different layers we reached a really small improvement in train and val dataset and almost same performance in test\n",
        "\n",
        "Experiment Name | Train Accuracy (Avg/StdDev)| Val Accuracy (Avg/StdDev)| Test accuracy (Avg/StdDev)|\n",
        "--- | --- | --- | ---\n",
        "RGB-D | 91.92 % / 3.64 % | 80.94% / 0.58 % | 75.79 % / 0.68 %\n",
        "RGB-D-fusion | 92.86 % / 1.55 % | 81.43% / 0.55 % | 75.75 % / 0.69 %"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9q9HJnBwJ_IZ",
        "colab_type": "text"
      },
      "source": [
        "## Fusion at different layers small network\n",
        "\n",
        "We will follow the same approach but we will try to connect earlier layers from the convolutional branches because more local features might be used to differentiate the dataset.\n",
        "\n",
        "We also have a simpler classifier layer that is implemented at the Fully Connected Part of the 'Fusion branch'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOq3-3FGbnm2",
        "colab_type": "text"
      },
      "source": [
        "### Adapted network architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gDqTVBiJapZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FIRST_CONV_LAYER_IDX_OUT = 6\n",
        "SECOND_CONV_LAYER_IDX_OUT = 8\n",
        "\n",
        "# In PyTorch every network is implementd as a nn.Module\n",
        "class SmallRGBDFusionNet(nn.Module):\n",
        "    # The parameters are initialized in __init__(self, ...)\n",
        "    def __init__(self, rgb_branch, hha_branch, num_classes):\n",
        "        super(SmallRGBDFusionNet, self).__init__()\n",
        "        \n",
        "        # RGB branch\n",
        "        model_rgb = rgb_branch\n",
        "        self.rgb_convs = model_rgb.features\n",
        "        c = model_rgb.classifier\n",
        "        self.rgb_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        num_ftrs_rgb = c[4].out_features\n",
        "\n",
        "        # HHA branch\n",
        "        model_hha = hha_branch\n",
        "        self.hha_convs = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        self.hha_fcs = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5])\n",
        "        f = model_hha.features\n",
        "        c = model_hha.classifier\n",
        "        num_ftrs_hha = c[4].out_features\n",
        "        \n",
        "        #Branch to fusion features from the RGB and HHA branch\n",
        "        self.fusion_features = nn.Sequential(\n",
        "            #First convolution of features will receive as input the concatenated feature maps\n",
        "            #from the output of the penultimate convolutional layers of both RGB and HHA branches\n",
        "            nn.Conv2d(192 * 2, 384, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            #Second convolution of features will receive as input the concatenated feature maps\n",
        "            #from the output of the last convolutional layers of both RGB and HHA branches plus the result\n",
        "            #of the convolution of its previous layer\n",
        "            nn.Conv2d(384 * 3, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2)\n",
        "        )\n",
        "\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\n",
        "\n",
        "        self.fusion_fcs = nn.Sequential(\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(256 * 6 * 6, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, num_classes)\n",
        "        )\n",
        "\n",
        "    def forward_branch_conv(self, branch_convs, first_layer, last_layer, x):\n",
        "      for layer in branch_convs[first_layer:last_layer]:\n",
        "        x = layer(x)\n",
        "      return x\n",
        "\n",
        "    # The data flow is defined in forward. No need to specify backward operations (PyTorch takes care of them)\n",
        "    def forward(self, x):\n",
        "      x_rgb = x[0]\n",
        "      #Extract first feature maps to inject to fusion branch from RGB\n",
        "      x_rgb = self.forward_branch_conv(self.rgb_convs, 0, FIRST_CONV_LAYER_IDX_OUT, x_rgb)\n",
        "      x_hha = x[1]\n",
        "      #Extract first feature maps to inject to fusion branch from HHA\n",
        "      x_hha = self.forward_branch_conv(self.hha_convs, 0, FIRST_CONV_LAYER_IDX_OUT, x_hha)\n",
        "      #Concatenate first extracted feature maps and pass them as input to fusion_features branch\n",
        "      x_fusion = torch.cat((x_rgb, x_hha), 1)\n",
        "      x_fusion = self.forward_branch_conv(self.fusion_features, 0, 2, x_fusion)\n",
        "      #Extract second feature maps to inject to fusion branch from RGB\n",
        "      x_rgb = self.forward_branch_conv(self.rgb_convs, FIRST_CONV_LAYER_IDX_OUT, SECOND_CONV_LAYER_IDX_OUT, x_rgb)\n",
        "      #Extract second feature maps to inject to fusion branch from HHA\n",
        "      x_hha = self.forward_branch_conv(self.hha_convs, FIRST_CONV_LAYER_IDX_OUT, SECOND_CONV_LAYER_IDX_OUT, x_hha)\n",
        "      #Reinject to fusion_features the second extracted feature maps from the branches\n",
        "      x_fusion = torch.cat((x_fusion, torch.cat((x_rgb, x_hha), 1)), 1)\n",
        "      x_fusion = self.forward_branch_conv(self.fusion_features, 2, len(self.fusion_features), x_fusion)\n",
        "      x_fusion = self.avgpool(x_fusion)\n",
        "      x_fusion = torch.flatten(x_fusion, 1)\n",
        "      x_fusion = self.fusion_fcs(x_fusion)\n",
        "      return x_fusion"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43s-FOzkJe5k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision.models\n",
        "\n",
        "def initialize_model_fusion_small():\n",
        "  # Instantiate the model\n",
        "  num_classes = len(class_names)\n",
        "  model = SmallRGBDFusionNet(rgb_branch=models.alexnet(pretrained=True),\n",
        "                      hha_branch=models.alexnet(pretrained=True), \n",
        "                      num_classes=num_classes)\n",
        "\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate = 0.001\n",
        "\n",
        "  for param in model.fusion_features.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  for param in model.fusion_fcs.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  #for param in model.classifier.parameters():\n",
        "  #  param.requires_grad = True\n",
        "    \n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "  return model, criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62ZY5BZmcFSJ",
        "colab_type": "text"
      },
      "source": [
        "### Train And evaluate small fusion network 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6HucASeJoh3",
        "colab_type": "code",
        "outputId": "1f9fbeee-301e-43ae-98f3-a4276e321738",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "results = list(map(lambda x: trainAndEvaluateMultiModal(multimodal_dataloaders, initialize_model_fusion_small, num_epochs=35), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 9.5494 Acc: 0.3624\n",
            "Phase val\n",
            "val Loss: 1.4383 Acc: 0.3237\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.2593 Acc: 0.4819\n",
            "Phase val\n",
            "val Loss: 0.9900 Acc: 0.6659\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.0820 Acc: 0.5906\n",
            "Phase val\n",
            "val Loss: 0.9699 Acc: 0.6486\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8732 Acc: 0.6832\n",
            "Phase val\n",
            "val Loss: 0.8044 Acc: 0.7039\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6742 Acc: 0.7450\n",
            "Phase val\n",
            "val Loss: 0.7363 Acc: 0.7627\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7348 Acc: 0.7248\n",
            "Phase val\n",
            "val Loss: 0.6941 Acc: 0.7592\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6406 Acc: 0.7584\n",
            "Phase val\n",
            "val Loss: 0.6536 Acc: 0.7650\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5905 Acc: 0.7839\n",
            "Phase val\n",
            "val Loss: 0.7378 Acc: 0.7500\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6087 Acc: 0.7852\n",
            "Phase val\n",
            "val Loss: 0.7922 Acc: 0.6728\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5988 Acc: 0.7638\n",
            "Phase val\n",
            "val Loss: 0.6748 Acc: 0.7892\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4968 Acc: 0.8107\n",
            "Phase val\n",
            "val Loss: 0.6770 Acc: 0.7949\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4732 Acc: 0.8081\n",
            "Phase val\n",
            "val Loss: 0.7322 Acc: 0.7938\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5344 Acc: 0.7960\n",
            "Phase val\n",
            "val Loss: 0.7287 Acc: 0.7500\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5026 Acc: 0.8040\n",
            "Phase val\n",
            "val Loss: 0.7053 Acc: 0.7719\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4392 Acc: 0.8416\n",
            "Phase val\n",
            "val Loss: 0.7235 Acc: 0.7765\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3925 Acc: 0.8470\n",
            "Phase val\n",
            "val Loss: 0.7992 Acc: 0.7442\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4416 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.8410 Acc: 0.7523\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4343 Acc: 0.8295\n",
            "Phase val\n",
            "val Loss: 0.7124 Acc: 0.7569\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4010 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.9054 Acc: 0.7523\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3650 Acc: 0.8671\n",
            "Phase val\n",
            "val Loss: 0.9589 Acc: 0.7477\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3281 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.9875 Acc: 0.7143\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3385 Acc: 0.8617\n",
            "Phase val\n",
            "val Loss: 1.0206 Acc: 0.6947\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3380 Acc: 0.8752\n",
            "Phase val\n",
            "val Loss: 0.8960 Acc: 0.7535\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2806 Acc: 0.8993\n",
            "Phase val\n",
            "val Loss: 1.0235 Acc: 0.7373\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3575 Acc: 0.8617\n",
            "Phase val\n",
            "val Loss: 0.9561 Acc: 0.7316\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3284 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 0.7801 Acc: 0.7869\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2749 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 1.0063 Acc: 0.7224\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2932 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 1.8492 Acc: 0.6624\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3212 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 1.0761 Acc: 0.7051\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3131 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 1.0262 Acc: 0.7535\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3309 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 1.1095 Acc: 0.7788\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3059 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 1.0576 Acc: 0.7857\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2744 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 1.0651 Acc: 0.7339\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2782 Acc: 0.8913\n",
            "Phase val\n",
            "val Loss: 1.0914 Acc: 0.7707\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2356 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.1864 Acc: 0.7535\n",
            "\n",
            "Training complete in 3m 19s\n",
            "Best val Acc: 0.794931\n",
            "\n",
            "Tested in 0m 3s Loss: 0.4486 Acc: 0.8389\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6770 Acc: 0.7949\n",
            "\n",
            "Tested in 0m 5s Loss: 0.8793 Acc: 0.7196\n",
            "Accuracy. Train: 83.89% val: 79.49% test: 71.96%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 22.2124 Acc: 0.3557\n",
            "Phase val\n",
            "val Loss: 1.3730 Acc: 0.4124\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.1313 Acc: 0.5530\n",
            "Phase val\n",
            "val Loss: 1.1818 Acc: 0.5795\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8883 Acc: 0.6671\n",
            "Phase val\n",
            "val Loss: 0.7689 Acc: 0.7304\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7338 Acc: 0.7262\n",
            "Phase val\n",
            "val Loss: 0.7215 Acc: 0.7120\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6491 Acc: 0.7503\n",
            "Phase val\n",
            "val Loss: 0.8040 Acc: 0.7500\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6086 Acc: 0.7745\n",
            "Phase val\n",
            "val Loss: 0.6394 Acc: 0.7592\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4967 Acc: 0.7946\n",
            "Phase val\n",
            "val Loss: 0.7940 Acc: 0.7477\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4371 Acc: 0.8228\n",
            "Phase val\n",
            "val Loss: 0.8966 Acc: 0.7005\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4876 Acc: 0.8148\n",
            "Phase val\n",
            "val Loss: 0.7044 Acc: 0.7546\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4416 Acc: 0.8295\n",
            "Phase val\n",
            "val Loss: 0.7306 Acc: 0.7776\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4446 Acc: 0.8322\n",
            "Phase val\n",
            "val Loss: 0.7010 Acc: 0.7074\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3889 Acc: 0.8443\n",
            "Phase val\n",
            "val Loss: 0.8563 Acc: 0.7189\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4481 Acc: 0.8255\n",
            "Phase val\n",
            "val Loss: 0.7086 Acc: 0.7546\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4055 Acc: 0.8389\n",
            "Phase val\n",
            "val Loss: 0.8932 Acc: 0.7684\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4519 Acc: 0.8336\n",
            "Phase val\n",
            "val Loss: 0.7377 Acc: 0.7707\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3849 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.6982 Acc: 0.7788\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3352 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.6984 Acc: 0.7984\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2944 Acc: 0.8966\n",
            "Phase val\n",
            "val Loss: 0.8944 Acc: 0.7604\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2752 Acc: 0.8886\n",
            "Phase val\n",
            "val Loss: 0.9385 Acc: 0.7707\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3761 Acc: 0.8591\n",
            "Phase val\n",
            "val Loss: 0.9350 Acc: 0.7846\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4545 Acc: 0.8403\n",
            "Phase val\n",
            "val Loss: 0.8490 Acc: 0.7765\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3996 Acc: 0.8537\n",
            "Phase val\n",
            "val Loss: 0.8517 Acc: 0.6947\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3105 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 0.9379 Acc: 0.7569\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2909 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 0.8459 Acc: 0.8007\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3173 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.8903 Acc: 0.7869\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2859 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 0.8529 Acc: 0.7926\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2449 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 0.8179 Acc: 0.7719\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2630 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 0.7943 Acc: 0.7800\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2486 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 0.8347 Acc: 0.7811\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2494 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 0.9090 Acc: 0.7730\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2054 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 0.8620 Acc: 0.7880\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2436 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 0.8505 Acc: 0.7972\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2061 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.0757 Acc: 0.7984\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2739 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 0.8977 Acc: 0.7880\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2300 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 0.7375 Acc: 0.7995\n",
            "\n",
            "Training complete in 3m 19s\n",
            "Best val Acc: 0.800691\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2764 Acc: 0.8940\n",
            "\n",
            "Tested in 0m 3s Loss: 0.8459 Acc: 0.8007\n",
            "\n",
            "Tested in 0m 5s Loss: 1.0699 Acc: 0.7461\n",
            "Accuracy. Train: 89.40% val: 80.07% test: 74.61%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 12.2450 Acc: 0.4161\n",
            "Phase val\n",
            "val Loss: 1.1738 Acc: 0.5783\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9886 Acc: 0.6282\n",
            "Phase val\n",
            "val Loss: 1.0037 Acc: 0.5484\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7861 Acc: 0.6832\n",
            "Phase val\n",
            "val Loss: 0.8720 Acc: 0.7166\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6867 Acc: 0.7383\n",
            "Phase val\n",
            "val Loss: 0.7634 Acc: 0.7362\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5832 Acc: 0.7933\n",
            "Phase val\n",
            "val Loss: 0.7494 Acc: 0.7477\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5576 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 0.7860 Acc: 0.7304\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5431 Acc: 0.7812\n",
            "Phase val\n",
            "val Loss: 0.9255 Acc: 0.6786\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4845 Acc: 0.8201\n",
            "Phase val\n",
            "val Loss: 0.8674 Acc: 0.7131\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4449 Acc: 0.8255\n",
            "Phase val\n",
            "val Loss: 0.9324 Acc: 0.7316\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3901 Acc: 0.8510\n",
            "Phase val\n",
            "val Loss: 1.1211 Acc: 0.7085\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4199 Acc: 0.8389\n",
            "Phase val\n",
            "val Loss: 0.8785 Acc: 0.7776\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4288 Acc: 0.8456\n",
            "Phase val\n",
            "val Loss: 0.9786 Acc: 0.6820\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4011 Acc: 0.8470\n",
            "Phase val\n",
            "val Loss: 1.1171 Acc: 0.7627\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4033 Acc: 0.8510\n",
            "Phase val\n",
            "val Loss: 0.8758 Acc: 0.7373\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3149 Acc: 0.8832\n",
            "Phase val\n",
            "val Loss: 1.1755 Acc: 0.6982\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2713 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.1572 Acc: 0.7097\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2701 Acc: 0.8926\n",
            "Phase val\n",
            "val Loss: 1.2135 Acc: 0.7258\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3777 Acc: 0.8537\n",
            "Phase val\n",
            "val Loss: 0.9285 Acc: 0.7362\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3211 Acc: 0.8779\n",
            "Phase val\n",
            "val Loss: 0.8718 Acc: 0.7419\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2592 Acc: 0.9007\n",
            "Phase val\n",
            "val Loss: 1.0532 Acc: 0.7408\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3519 Acc: 0.8779\n",
            "Phase val\n",
            "val Loss: 1.0311 Acc: 0.7442\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3353 Acc: 0.8792\n",
            "Phase val\n",
            "val Loss: 0.8749 Acc: 0.7350\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2693 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 0.9390 Acc: 0.7512\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2414 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.0435 Acc: 0.7811\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2310 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.1914 Acc: 0.7535\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2485 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 1.0846 Acc: 0.7696\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2060 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 1.1745 Acc: 0.7316\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2309 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.8453 Acc: 0.6993\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2744 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 1.3972 Acc: 0.7304\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2473 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.0802 Acc: 0.7638\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1899 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 1.1329 Acc: 0.7385\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1948 Acc: 0.9356\n",
            "Phase val\n",
            "val Loss: 1.4321 Acc: 0.7776\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1902 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.7288 Acc: 0.7304\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2344 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.1600 Acc: 0.7707\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2304 Acc: 0.9208\n",
            "Phase val\n",
            "val Loss: 1.2357 Acc: 0.7396\n",
            "\n",
            "Training complete in 3m 20s\n",
            "Best val Acc: 0.781106\n",
            "\n",
            "Tested in 0m 3s Loss: 0.2378 Acc: 0.9154\n",
            "\n",
            "Tested in 0m 3s Loss: 1.0435 Acc: 0.7811\n",
            "\n",
            "Tested in 0m 5s Loss: 1.5093 Acc: 0.7158\n",
            "Accuracy. Train: 91.54% val: 78.11% test: 71.58%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 12.4332 Acc: 0.4148\n",
            "Phase val\n",
            "val Loss: 1.3125 Acc: 0.4228\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.9475 Acc: 0.6443\n",
            "Phase val\n",
            "val Loss: 0.9241 Acc: 0.6279\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7889 Acc: 0.7141\n",
            "Phase val\n",
            "val Loss: 1.1929 Acc: 0.6256\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.7185 Acc: 0.7101\n",
            "Phase val\n",
            "val Loss: 0.8955 Acc: 0.6498\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5810 Acc: 0.7772\n",
            "Phase val\n",
            "val Loss: 0.7819 Acc: 0.6970\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5774 Acc: 0.7973\n",
            "Phase val\n",
            "val Loss: 0.8310 Acc: 0.7247\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5050 Acc: 0.8027\n",
            "Phase val\n",
            "val Loss: 0.8859 Acc: 0.6797\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4889 Acc: 0.8201\n",
            "Phase val\n",
            "val Loss: 0.8550 Acc: 0.6878\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4808 Acc: 0.8094\n",
            "Phase val\n",
            "val Loss: 0.8517 Acc: 0.7097\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4545 Acc: 0.8362\n",
            "Phase val\n",
            "val Loss: 0.7940 Acc: 0.7235\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4601 Acc: 0.8228\n",
            "Phase val\n",
            "val Loss: 0.8866 Acc: 0.6970\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3371 Acc: 0.8698\n",
            "Phase val\n",
            "val Loss: 0.9635 Acc: 0.6912\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3720 Acc: 0.8604\n",
            "Phase val\n",
            "val Loss: 1.1050 Acc: 0.7189\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4230 Acc: 0.8416\n",
            "Phase val\n",
            "val Loss: 0.7735 Acc: 0.7788\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3610 Acc: 0.8564\n",
            "Phase val\n",
            "val Loss: 0.9173 Acc: 0.7235\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2986 Acc: 0.8926\n",
            "Phase val\n",
            "val Loss: 1.2412 Acc: 0.6394\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3304 Acc: 0.8671\n",
            "Phase val\n",
            "val Loss: 0.7952 Acc: 0.7546\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3381 Acc: 0.8819\n",
            "Phase val\n",
            "val Loss: 0.8885 Acc: 0.7535\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3143 Acc: 0.8698\n",
            "Phase val\n",
            "val Loss: 0.9722 Acc: 0.7339\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3117 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 1.1902 Acc: 0.6843\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3226 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 1.2525 Acc: 0.6740\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3171 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 1.0813 Acc: 0.7177\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3029 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 1.1421 Acc: 0.7235\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2758 Acc: 0.8859\n",
            "Phase val\n",
            "val Loss: 1.2036 Acc: 0.7224\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2717 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 1.4130 Acc: 0.7074\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2453 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 0.9048 Acc: 0.7650\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2133 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.0592 Acc: 0.7753\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2596 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.1949 Acc: 0.7431\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2247 Acc: 0.9195\n",
            "Phase val\n",
            "val Loss: 1.0048 Acc: 0.7661\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2314 Acc: 0.9154\n",
            "Phase val\n",
            "val Loss: 1.2491 Acc: 0.7350\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2717 Acc: 0.9087\n",
            "Phase val\n",
            "val Loss: 1.1427 Acc: 0.7419\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1882 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.2566 Acc: 0.7488\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1977 Acc: 0.9235\n",
            "Phase val\n",
            "val Loss: 1.4165 Acc: 0.7327\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2604 Acc: 0.8953\n",
            "Phase val\n",
            "val Loss: 1.7970 Acc: 0.6763\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2358 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.3878 Acc: 0.6993\n",
            "\n",
            "Training complete in 3m 18s\n",
            "Best val Acc: 0.778802\n",
            "\n",
            "Tested in 0m 3s Loss: 0.3313 Acc: 0.8711\n",
            "\n",
            "Tested in 0m 3s Loss: 0.7735 Acc: 0.7788\n",
            "\n",
            "Tested in 0m 5s Loss: 0.9631 Acc: 0.7284\n",
            "Accuracy. Train: 87.11% val: 77.88% test: 72.84%\n",
            "Epoch 0/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 10.9854 Acc: 0.4054\n",
            "Phase val\n",
            "val Loss: 0.9545 Acc: 0.6740\n",
            "\n",
            "Epoch 1/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 1.0083 Acc: 0.6081\n",
            "Phase val\n",
            "val Loss: 0.8620 Acc: 0.6959\n",
            "\n",
            "Epoch 2/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8195 Acc: 0.6913\n",
            "Phase val\n",
            "val Loss: 0.9800 Acc: 0.6544\n",
            "\n",
            "Epoch 3/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6172 Acc: 0.7477\n",
            "Phase val\n",
            "val Loss: 1.0171 Acc: 0.7039\n",
            "\n",
            "Epoch 4/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.6510 Acc: 0.7463\n",
            "Phase val\n",
            "val Loss: 0.7779 Acc: 0.7131\n",
            "\n",
            "Epoch 5/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5848 Acc: 0.7852\n",
            "Phase val\n",
            "val Loss: 0.8697 Acc: 0.7143\n",
            "\n",
            "Epoch 6/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5361 Acc: 0.7812\n",
            "Phase val\n",
            "val Loss: 1.1598 Acc: 0.6486\n",
            "\n",
            "Epoch 7/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5037 Acc: 0.7987\n",
            "Phase val\n",
            "val Loss: 0.8722 Acc: 0.7546\n",
            "\n",
            "Epoch 8/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5699 Acc: 0.7879\n",
            "Phase val\n",
            "val Loss: 0.8238 Acc: 0.7592\n",
            "\n",
            "Epoch 9/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4326 Acc: 0.8443\n",
            "Phase val\n",
            "val Loss: 0.9223 Acc: 0.7385\n",
            "\n",
            "Epoch 10/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3888 Acc: 0.8564\n",
            "Phase val\n",
            "val Loss: 1.0677 Acc: 0.6970\n",
            "\n",
            "Epoch 11/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4030 Acc: 0.8497\n",
            "Phase val\n",
            "val Loss: 0.7976 Acc: 0.7500\n",
            "\n",
            "Epoch 12/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3618 Acc: 0.8644\n",
            "Phase val\n",
            "val Loss: 0.8985 Acc: 0.7396\n",
            "\n",
            "Epoch 13/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3567 Acc: 0.8711\n",
            "Phase val\n",
            "val Loss: 1.0789 Acc: 0.7408\n",
            "\n",
            "Epoch 14/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3233 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 0.8790 Acc: 0.7592\n",
            "\n",
            "Epoch 15/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3438 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 1.0407 Acc: 0.7362\n",
            "\n",
            "Epoch 16/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3563 Acc: 0.8550\n",
            "Phase val\n",
            "val Loss: 0.8050 Acc: 0.7500\n",
            "\n",
            "Epoch 17/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3102 Acc: 0.8926\n",
            "Phase val\n",
            "val Loss: 0.9731 Acc: 0.7396\n",
            "\n",
            "Epoch 18/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3091 Acc: 0.8765\n",
            "Phase val\n",
            "val Loss: 1.3107 Acc: 0.6578\n",
            "\n",
            "Epoch 19/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4488 Acc: 0.8389\n",
            "Phase val\n",
            "val Loss: 1.0598 Acc: 0.7465\n",
            "\n",
            "Epoch 20/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3586 Acc: 0.8617\n",
            "Phase val\n",
            "val Loss: 1.0386 Acc: 0.7339\n",
            "\n",
            "Epoch 21/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3293 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 1.1127 Acc: 0.7085\n",
            "\n",
            "Epoch 22/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3537 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 1.1327 Acc: 0.6763\n",
            "\n",
            "Epoch 23/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2640 Acc: 0.9007\n",
            "Phase val\n",
            "val Loss: 1.0607 Acc: 0.6993\n",
            "\n",
            "Epoch 24/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2490 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.2875 Acc: 0.7028\n",
            "\n",
            "Epoch 25/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2944 Acc: 0.8872\n",
            "Phase val\n",
            "val Loss: 1.0352 Acc: 0.7396\n",
            "\n",
            "Epoch 26/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2391 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 1.1203 Acc: 0.7316\n",
            "\n",
            "Epoch 27/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2622 Acc: 0.9020\n",
            "Phase val\n",
            "val Loss: 1.1054 Acc: 0.7350\n",
            "\n",
            "Epoch 28/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2445 Acc: 0.9101\n",
            "Phase val\n",
            "val Loss: 1.2533 Acc: 0.7339\n",
            "\n",
            "Epoch 29/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1981 Acc: 0.9289\n",
            "Phase val\n",
            "val Loss: 1.5578 Acc: 0.6694\n",
            "\n",
            "Epoch 30/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2662 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.7567 Acc: 0.6452\n",
            "\n",
            "Epoch 31/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2989 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 1.5185 Acc: 0.6855\n",
            "\n",
            "Epoch 32/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2502 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.1517 Acc: 0.7454\n",
            "\n",
            "Epoch 33/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2526 Acc: 0.8980\n",
            "Phase val\n",
            "val Loss: 1.2418 Acc: 0.7316\n",
            "\n",
            "Epoch 34/34\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2671 Acc: 0.9047\n",
            "Phase val\n",
            "val Loss: 1.4321 Acc: 0.6982\n",
            "\n",
            "Training complete in 3m 20s\n",
            "Best val Acc: 0.759217\n",
            "\n",
            "Tested in 0m 3s Loss: 0.4631 Acc: 0.8188\n",
            "\n",
            "Tested in 0m 3s Loss: 0.8238 Acc: 0.7592\n",
            "\n",
            "Tested in 0m 5s Loss: 0.9435 Acc: 0.7095\n",
            "Accuracy. Train: 81.88% val: 75.92% test: 70.95%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74pF2uQBJldA",
        "colab_type": "code",
        "outputId": "64cb2a4d-44be-42ea-debe-c34cc847ed48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test RGB-D with Fusion at different levels accuracy (small) {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training RGB-D with Fusion at different levels accuracy (small) 86.76509857177734%\n",
            "StDev training RGB-D with Fusion at different levels accuracy (small) 3.5213861614465714%\n",
            "Mean validation RGB-D with Fusion at different levels accuracy (small) 78.29493284225464%\n",
            "StDev validation RGB-D with Fusion at different levels accuracy (small) 1.4437275938689709%\n",
            "Mean test RGB-D with Fusion at different levels accuracy (small) 72.38815426826477%\n",
            "StDev test RGB-D with Fusion at different levels accuracy (small) 1.266776118427515%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1WZVPNjVm3O",
        "colab_type": "text"
      },
      "source": [
        "We have decreased the amount of parameters without loosing much accuracy but we still did not match to outperform the RGB-D approach."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkSI0qPwlCX_",
        "colab_type": "text"
      },
      "source": [
        "## Knowledge distilation from RGB to HHA domain\n",
        "\n",
        "We try to take advantage of the RGB data to improve performance of the model that uses only HHA (depth) data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z29KHbuEbM9C",
        "colab_type": "text"
      },
      "source": [
        "###Merged Dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-AdhS84pWOo",
        "colab_type": "text"
      },
      "source": [
        "We need to define a new DataSet class that will get the same corresponding image from the HHA dataset and RGB dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU-CrrU6Wykh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torchvision.datasets.vision import VisionDataset\n",
        "class MergedImageFolder(VisionDataset):\n",
        "  def __init__(self, hha_dataset, rgb_dataset):\n",
        "    self.classes = hha_dataset.classes\n",
        "    self.class_to_idx = hha_dataset.class_to_idx\n",
        "    self.hha_dataset = hha_dataset\n",
        "    self.rgb_dataset = rgb_dataset\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    hha_item, target = self.hha_dataset.__getitem__(index)\n",
        "    rgb_item, target = self.rgb_dataset.__getitem__(index)\n",
        "    return hha_item, rgb_item, target\n",
        "\n",
        "  def __len__(self):\n",
        "    return self.rgb_dataset.__len__()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9X0D65WabRJ2",
        "colab_type": "text"
      },
      "source": [
        "###KLDivLoss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cw0rsr0pgjL",
        "colab_type": "text"
      },
      "source": [
        "We need to have a new Loss KLDivLoss where tha amount of information from hard labels and dense log probs vectors is controlled by the parameter ALPHA (The larger the ALPHA, the most important is the distilled knowledge (Will be set to 0.8 to study better the effect of distillation). Then there is the parameter T (Temperature of the probability density function) that will be set to 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-K-EA2GJnNfj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "ALPHA = 0.8\n",
        "T = 1\n",
        "def loss_fn_kd(outputs, labels, teacher_outputs):\n",
        "  KD_loss = nn.KLDivLoss()(F.log_softmax(outputs/T, dim=1),\n",
        "                           F.softmax(teacher_outputs/T, dim=1)) * (ALPHA * T * T) + \\\n",
        "            F.cross_entropy(outputs, labels) * (1. - ALPHA)\n",
        "\n",
        "  return KD_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4H4OhelYwL1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data augmentation and normalization for training\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'test': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n",
        "\n",
        "# Path to the dataset\n",
        "data_dir = 'sunrgbd_lite'\n",
        "\n",
        "# Preparing dataset and dataloaders\n",
        "partitions = ['train', 'val', 'test']\n",
        "rgb_datasets = {x: torchvision.datasets.ImageFolder(os.path.join(data_dir, x, 'rgb'),\n",
        "                                          data_transforms[x])\n",
        "                  for x in partitions}\n",
        "\n",
        "hha_datasets = {x: torchvision.datasets.ImageFolder(os.path.join(data_dir, x, 'hha'),\n",
        "                                          data_transforms[x])\n",
        "                  for x in partitions}\n",
        "\n",
        "#Merge dataset composed of both RGB and HHA dataset\n",
        "merge_datasets = {x: MergedImageFolder(hha_datasets[x], rgb_datasets[x])\n",
        "                  for x in partitions}\n",
        "\n",
        "\n",
        "merge_dataloaders = {x: torch.utils.data.DataLoader(merge_datasets[x], batch_size=4,\n",
        "                                                shuffle=True, num_workers=4)\n",
        "                  for x in partitions}\n",
        "\n",
        "dataset_sizes = {x: len(merge_datasets[x]) for x in partitions}\n",
        "\n",
        "class_names = merge_datasets['train'].classes\n",
        "\n",
        "cuda = torch.cuda.is_available()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhniBqoZbVfZ",
        "colab_type": "text"
      },
      "source": [
        "### Adapted training utilities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJLK9hREn4ab",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def transfer_learning_train_epoch(dataloaders, epoch, teacher_model, student_model, scheduler, optimizer, criterion, best_model_wts, best_acc):\n",
        "  # Each epoch has a training and validation phase\n",
        "  for phase in ['train', 'val']:\n",
        "    print('Phase %s' % phase)\n",
        "    if phase == 'train':\n",
        "      if scheduler != None:\n",
        "        scheduler.step()\n",
        "        student_model.train(True)  # Set model to training mode\n",
        "      else:\n",
        "        student_model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "    teacher_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for inputs_hha, inputs_rgb, labels in dataloaders[phase]:\n",
        "      if cuda:\n",
        "        inputs_hha = Variable(inputs_hha.cuda())\n",
        "        inputs_rgb = Variable(inputs_rgb.cuda())\n",
        "        labels = Variable(labels.cuda())\n",
        "      else:\n",
        "        inputs_hha, inputs_rgb, labels = Variable(inputs_hha), Variable(inputs_rgb), Variable(labels)\n",
        "\n",
        "      # zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # forward\n",
        "      outputs = student_model(inputs_hha)\n",
        "      teacher_outputs = teacher_model(inputs_rgb)\n",
        "\n",
        "      _, preds = torch.max(outputs.data, 1)\n",
        "      _, preds_teacher = torch.max(teacher_outputs.data, 1)\n",
        "      loss = criterion(outputs, labels, teacher_outputs)\n",
        "\n",
        "      # backward + optimize only if in training phase\n",
        "      if phase == 'train':\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "      # statistics\n",
        "      # running_loss += loss.data[0] * inputs.size(0) # Pytorch 0.4\n",
        "      running_loss += loss.data.item() * inputs_hha.size(0) # Pytorch 1\n",
        "      running_corrects += torch.sum(preds == labels.data)\n",
        "      teacher_corrects += torch.sum(preds_teacher == labels.data)\n",
        "\n",
        "    epoch_loss = running_loss / dataset_sizes[phase]\n",
        "    epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "    epoch_teacher_acc = teacher_corrects / dataset_sizes[phase]\n",
        "\n",
        "    print('{} Loss: {:.4f} Acc: {:.4f}, Teacher Acc (Sanity check): {:.4f}'.format(phase, epoch_loss, epoch_acc, epoch_teacher_acc))\n",
        "\n",
        "    # deep copy the model\n",
        "    if phase == 'val' and epoch_acc > best_acc:\n",
        "        best_acc = epoch_acc\n",
        "        best_model_wts = copy.deepcopy(student_model.state_dict())\n",
        "\n",
        "  print()\n",
        "  return best_model_wts, best_acc\n",
        "\n",
        "def transfer_learning_train_model(dataloaders, teacher_model, student_model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "    teacher_model.eval()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(student_model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "        best_model_wts, best_acc = transfer_learning_train_epoch(dataloaders, epoch, teacher_model, student_model, scheduler, optimizer, criterion, best_model_wts, best_acc)\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    student_model.load_state_dict(best_model_wts)\n",
        "    return student_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMc0jMSaniNm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_transfer_learning_model(dataloaders, model, partition, criterion=nn.CrossEntropyLoss()):\n",
        "    since = time.time()\n",
        "\n",
        "    model.train(False)  # Set model to evaluate mode\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0.0\n",
        "\n",
        "    # Iterate over data.\n",
        "    for data in dataloaders[partition]:\n",
        "        # get the inputs\n",
        "        inputs, _, labels = data\n",
        "        # wrap them in Variable\n",
        "        if cuda:\n",
        "            inputs = Variable(inputs.cuda())\n",
        "            labels = Variable(labels.cuda())\n",
        "        else:\n",
        "            inputs, labels = Variable(inputs), Variable(labels)\n",
        "\n",
        "        # forward\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs.data, 1)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # statistics\n",
        "        # running_loss += loss.data[0] * inputs.size(0) # Pytorch 0.4\n",
        "        running_loss += loss.data.item() * inputs.size(0) # Pytorch 1\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "    test_loss = running_loss / rgb_dataset_sizes[partition]\n",
        "    test_acc = running_corrects / rgb_dataset_sizes[partition]\n",
        "    \n",
        "    print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Tested in {:.0f}m {:.0f}s Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60, test_loss, test_acc))\n",
        "\n",
        "    return test_acc, test_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAGo8QmXtfvh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def initialize_model_transfer_learning():\n",
        "  model = models.alexnet(pretrained=True)\n",
        "\n",
        "  for param in model.parameters(): # Freeze all parameters by default\n",
        "    param.requires_grad = False\n",
        "    \n",
        "  num_classes = len(class_names)\n",
        "  c = model.classifier\n",
        "  num_ftrs = c[6].in_features\n",
        "  model.classifier = nn.Sequential(c[0],c[1],c[2],c[3],c[4],c[5],nn.Linear(num_ftrs, num_classes))\n",
        "\n",
        "  if cuda:\n",
        "    model = model.cuda()\n",
        "\n",
        "  criterion = loss_fn_kd\n",
        "  criterion_eval = nn.CrossEntropyLoss()\n",
        "\n",
        "  learning_rate =0.001\n",
        "\n",
        "  for param in model.features.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  for param in model.classifier.parameters():\n",
        "    param.requires_grad = True\n",
        "\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "  return model, criterion, criterion_eval, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hg6tO-4sys0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train For\n",
        "def trainAndEvaluateKDModel(dalaloaders, teacher_model, initialize_model):\n",
        "  model, criterion, criterion_eval, optimizer = initialize_model()\n",
        "  model = transfer_learning_train_model(dalaloaders, teacher_model, model, criterion, optimizer, None, num_epochs=25)\n",
        "    \n",
        "  # Evaluate\n",
        "  train_acc, _ = evaluate_transfer_learning_model(dalaloaders, model, 'train', criterion_eval)\n",
        "  val_acc, _ = evaluate_transfer_learning_model(dalaloaders, model, 'val', criterion_eval)\n",
        "  test_acc, _ = evaluate_transfer_learning_model(dalaloaders, model, 'test', criterion_eval)\n",
        "  print('Accuracy. Train: %1.2f%% val: %1.2f%% test: %1.2f%%' % \n",
        "        (train_acc*100, val_acc*100, test_acc*100))\n",
        "  return train_acc, val_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McOVYPASbbQ8",
        "colab_type": "text"
      },
      "source": [
        "###Train a teacher network using RGB Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHTqPTyURS7K",
        "colab_type": "code",
        "outputId": "c3606c5a-d074-45bb-cda8-96365980d79b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print ('Train teacher model')\n",
        "teacher_model, criterion, optimizer = initialize_model_monomodal()\n",
        "teacher_model = monomodal_train_model(rgb_dataloaders, teacher_model, criterion, optimizer, None, num_epochs=25)\n",
        "    \n",
        "# Evaluate\n",
        "train_acc, _ = evaluate_monomodal_model(rgb_dataloaders, teacher_model, 'train', criterion)\n",
        "val_acc, _ = evaluate_monomodal_model(rgb_dataloaders, teacher_model, 'val', criterion)\n",
        "test_acc, _ = evaluate_monomodal_model(rgb_dataloaders, teacher_model, 'test', criterion)\n",
        "print('Teacher model: Accuracy. Train teacher model: %1.2f%% val: %1.2f%% test: %1.2f%%' % \n",
        "        (train_acc*100, val_acc*100, test_acc*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train teacher model\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.8765 Acc: 0.6926\n",
            "Phase val\n",
            "val Loss: 0.7810 Acc: 0.7154\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5606 Acc: 0.8188\n",
            "Phase val\n",
            "val Loss: 0.8059 Acc: 0.7431\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4380 Acc: 0.8497\n",
            "Phase val\n",
            "val Loss: 0.8319 Acc: 0.7281\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3920 Acc: 0.8470\n",
            "Phase val\n",
            "val Loss: 0.9816 Acc: 0.7442\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3866 Acc: 0.8752\n",
            "Phase val\n",
            "val Loss: 0.7925 Acc: 0.7696\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3528 Acc: 0.8846\n",
            "Phase val\n",
            "val Loss: 1.2950 Acc: 0.6843\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3259 Acc: 0.8886\n",
            "Phase val\n",
            "val Loss: 0.9159 Acc: 0.7512\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2711 Acc: 0.9168\n",
            "Phase val\n",
            "val Loss: 1.3230 Acc: 0.6521\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2453 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.0582 Acc: 0.7523\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2079 Acc: 0.9302\n",
            "Phase val\n",
            "val Loss: 1.0494 Acc: 0.7512\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3421 Acc: 0.8899\n",
            "Phase val\n",
            "val Loss: 0.9480 Acc: 0.7419\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2911 Acc: 0.9007\n",
            "Phase val\n",
            "val Loss: 1.0450 Acc: 0.7258\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2154 Acc: 0.9074\n",
            "Phase val\n",
            "val Loss: 1.1131 Acc: 0.7431\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1972 Acc: 0.9329\n",
            "Phase val\n",
            "val Loss: 1.0414 Acc: 0.7385\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2869 Acc: 0.9060\n",
            "Phase val\n",
            "val Loss: 0.9894 Acc: 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3141 Acc: 0.9114\n",
            "Phase val\n",
            "val Loss: 1.1508 Acc: 0.7454\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2808 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.1128 Acc: 0.7800\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3076 Acc: 0.9034\n",
            "Phase val\n",
            "val Loss: 1.1875 Acc: 0.7442\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3074 Acc: 0.9141\n",
            "Phase val\n",
            "val Loss: 1.2498 Acc: 0.7028\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2020 Acc: 0.9409\n",
            "Phase val\n",
            "val Loss: 1.3096 Acc: 0.7362\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1884 Acc: 0.9383\n",
            "Phase val\n",
            "val Loss: 1.1710 Acc: 0.7546\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2358 Acc: 0.9181\n",
            "Phase val\n",
            "val Loss: 1.2390 Acc: 0.7454\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2739 Acc: 0.9128\n",
            "Phase val\n",
            "val Loss: 1.3643 Acc: 0.7442\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2901 Acc: 0.9221\n",
            "Phase val\n",
            "val Loss: 1.3807 Acc: 0.7200\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2421 Acc: 0.9248\n",
            "Phase val\n",
            "val Loss: 1.3309 Acc: 0.7189\n",
            "\n",
            "Training complete in 2m 9s\n",
            "Best val Acc: 0.782258\n",
            "\n",
            "Tested in 0m 1s Loss: 0.1802 Acc: 0.9329\n",
            "\n",
            "Tested in 0m 1s Loss: 0.9894 Acc: 0.7823\n",
            "\n",
            "Tested in 0m 2s Loss: 1.4106 Acc: 0.7064\n",
            "Teacher model: Accuracy. Train teacher model: 93.29% val: 78.23% test: 70.64%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJBv4RqubhM2",
        "colab_type": "text"
      },
      "source": [
        "### Train And evaluate student network 5 runs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZymWBPdZlQQe",
        "colab_type": "code",
        "outputId": "ba358758-01a7-4bd3-9355-4c51c143d6ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "print ('Train student models')\n",
        "if cuda:\n",
        "  teacher_model = teacher_model.cuda()\n",
        "results = list(map(lambda x: trainAndEvaluateKDModel(merge_dataloaders, teacher_model, initialize_model_transfer_learning), range(0, 5)))\n",
        "train_accs = list(map(lambda x: x[0], results))\n",
        "val_accs = list(map(lambda x: x[1], results))\n",
        "test_accs = list(map(lambda x: x[2], results))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train student models\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1958: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.\n",
            "  warnings.warn(\"reduction: 'mean' divides the total loss by both the batch size and the support size.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "train Loss: 0.5048 Acc: 0.4725, Teacher Acc (Sanity check): 0.9114\n",
            "Phase val\n",
            "val Loss: 0.8341 Acc: 0.2834, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4013 Acc: 0.5584, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.3571 Acc: 0.6164, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3612 Acc: 0.6161, Teacher Acc (Sanity check): 0.9329\n",
            "Phase val\n",
            "val Loss: 0.4848 Acc: 0.6025, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3220 Acc: 0.6376, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.3868 Acc: 0.6382, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3163 Acc: 0.6564, Teacher Acc (Sanity check): 0.9114\n",
            "Phase val\n",
            "val Loss: 0.3732 Acc: 0.6382, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2994 Acc: 0.6940, Teacher Acc (Sanity check): 0.9315\n",
            "Phase val\n",
            "val Loss: 0.3696 Acc: 0.5726, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2783 Acc: 0.7181, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.3904 Acc: 0.6728, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3056 Acc: 0.6792, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.3569 Acc: 0.6371, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2578 Acc: 0.7235, Teacher Acc (Sanity check): 0.9087\n",
            "Phase val\n",
            "val Loss: 0.3519 Acc: 0.6233, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2696 Acc: 0.7248, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.4367 Acc: 0.6394, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2609 Acc: 0.7208, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.4327 Acc: 0.6406, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2629 Acc: 0.7436, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.4037 Acc: 0.6141, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2481 Acc: 0.7503, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.3972 Acc: 0.6256, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2649 Acc: 0.7289, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4388 Acc: 0.6118, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2495 Acc: 0.7557, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.4129 Acc: 0.6866, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2449 Acc: 0.7611, Teacher Acc (Sanity check): 0.8953\n",
            "Phase val\n",
            "val Loss: 0.4748 Acc: 0.6613, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2390 Acc: 0.7718, Teacher Acc (Sanity check): 0.9154\n",
            "Phase val\n",
            "val Loss: 0.4095 Acc: 0.6129, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2298 Acc: 0.7705, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.3698 Acc: 0.6221, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2339 Acc: 0.7597, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.4188 Acc: 0.6671, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2199 Acc: 0.7758, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.4011 Acc: 0.6590, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2372 Acc: 0.7691, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.3976 Acc: 0.5968, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2063 Acc: 0.8027, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.5010 Acc: 0.6187, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2122 Acc: 0.7987, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.5315 Acc: 0.6463, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2207 Acc: 0.7732, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.4160 Acc: 0.6521, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2045 Acc: 0.8054, Teacher Acc (Sanity check): 0.9423\n",
            "Phase val\n",
            "val Loss: 0.4148 Acc: 0.6198, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Training complete in 3m 6s\n",
            "Best val Acc: 0.686636\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6176 Acc: 0.7718\n",
            "\n",
            "Tested in 0m 2s Loss: 1.1417 Acc: 0.6866\n",
            "\n",
            "Tested in 0m 4s Loss: 1.3435 Acc: 0.6471\n",
            "Accuracy. Train: 77.18% val: 68.66% test: 64.71%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5432 Acc: 0.4349, Teacher Acc (Sanity check): 0.9275\n",
            "Phase val\n",
            "val Loss: 0.4895 Acc: 0.4712, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3968 Acc: 0.5758, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.3506 Acc: 0.6647, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3543 Acc: 0.6027, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.4867 Acc: 0.4908, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3494 Acc: 0.6282, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.4934 Acc: 0.5046, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3270 Acc: 0.6322, Teacher Acc (Sanity check): 0.9342\n",
            "Phase val\n",
            "val Loss: 0.4001 Acc: 0.5645, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3136 Acc: 0.6685, Teacher Acc (Sanity check): 0.9101\n",
            "Phase val\n",
            "val Loss: 0.3267 Acc: 0.6740, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3043 Acc: 0.6872, Teacher Acc (Sanity check): 0.9074\n",
            "Phase val\n",
            "val Loss: 0.3756 Acc: 0.6187, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2900 Acc: 0.7060, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.3443 Acc: 0.6613, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2911 Acc: 0.7168, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.3696 Acc: 0.6590, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2911 Acc: 0.6886, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.5759 Acc: 0.4931, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3182 Acc: 0.6792, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.4269 Acc: 0.6613, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2670 Acc: 0.7195, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.3996 Acc: 0.6106, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2721 Acc: 0.7369, Teacher Acc (Sanity check): 0.9329\n",
            "Phase val\n",
            "val Loss: 0.4217 Acc: 0.6106, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2624 Acc: 0.7154, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.4534 Acc: 0.6325, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2443 Acc: 0.7450, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.4733 Acc: 0.6429, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2512 Acc: 0.7530, Teacher Acc (Sanity check): 0.9302\n",
            "Phase val\n",
            "val Loss: 0.4301 Acc: 0.6187, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2378 Acc: 0.7651, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.5218 Acc: 0.5945, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2471 Acc: 0.7584, Teacher Acc (Sanity check): 0.9195\n",
            "Phase val\n",
            "val Loss: 0.3968 Acc: 0.6336, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2343 Acc: 0.7678, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.3851 Acc: 0.5979, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2298 Acc: 0.7732, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.4455 Acc: 0.6187, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2403 Acc: 0.7758, Teacher Acc (Sanity check): 0.9060\n",
            "Phase val\n",
            "val Loss: 0.3682 Acc: 0.6382, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2246 Acc: 0.7812, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4251 Acc: 0.6555, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2203 Acc: 0.7866, Teacher Acc (Sanity check): 0.9409\n",
            "Phase val\n",
            "val Loss: 0.3690 Acc: 0.6452, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2356 Acc: 0.7651, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4376 Acc: 0.6279, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2236 Acc: 0.7960, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.4720 Acc: 0.6083, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Training complete in 3m 7s\n",
            "Best val Acc: 0.673963\n",
            "\n",
            "Tested in 0m 3s Loss: 0.8100 Acc: 0.7128\n",
            "\n",
            "Tested in 0m 3s Loss: 0.9601 Acc: 0.6740\n",
            "\n",
            "Tested in 0m 4s Loss: 1.0918 Acc: 0.6137\n",
            "Accuracy. Train: 71.28% val: 67.40% test: 61.37%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5068 Acc: 0.4711, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.3815 Acc: 0.5138, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4175 Acc: 0.5664, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.3621 Acc: 0.6071, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3682 Acc: 0.6188, Teacher Acc (Sanity check): 0.9450\n",
            "Phase val\n",
            "val Loss: 0.3457 Acc: 0.5760, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3189 Acc: 0.6591, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.7129 Acc: 0.4758, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3171 Acc: 0.6497, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.4069 Acc: 0.6037, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2940 Acc: 0.6846, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.3486 Acc: 0.6313, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2821 Acc: 0.7114, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.3614 Acc: 0.6325, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2806 Acc: 0.7315, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.3952 Acc: 0.5772, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2830 Acc: 0.6966, Teacher Acc (Sanity check): 0.9034\n",
            "Phase val\n",
            "val Loss: 0.3482 Acc: 0.6371, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2544 Acc: 0.6993, Teacher Acc (Sanity check): 0.9114\n",
            "Phase val\n",
            "val Loss: 0.5635 Acc: 0.5161, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2767 Acc: 0.7262, Teacher Acc (Sanity check): 0.9087\n",
            "Phase val\n",
            "val Loss: 0.3682 Acc: 0.5829, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2649 Acc: 0.7369, Teacher Acc (Sanity check): 0.9087\n",
            "Phase val\n",
            "val Loss: 0.3697 Acc: 0.6486, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2509 Acc: 0.7503, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.5091 Acc: 0.5461, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2604 Acc: 0.7450, Teacher Acc (Sanity check): 0.9007\n",
            "Phase val\n",
            "val Loss: 0.3556 Acc: 0.6233, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2369 Acc: 0.7691, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.3849 Acc: 0.6302, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2438 Acc: 0.7570, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.3791 Acc: 0.6141, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2449 Acc: 0.7530, Teacher Acc (Sanity check): 0.9356\n",
            "Phase val\n",
            "val Loss: 0.3883 Acc: 0.6210, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2225 Acc: 0.7638, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.3689 Acc: 0.6440, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2531 Acc: 0.7490, Teacher Acc (Sanity check): 0.9154\n",
            "Phase val\n",
            "val Loss: 0.4568 Acc: 0.5899, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2335 Acc: 0.7544, Teacher Acc (Sanity check): 0.9275\n",
            "Phase val\n",
            "val Loss: 0.3610 Acc: 0.6498, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2243 Acc: 0.7866, Teacher Acc (Sanity check): 0.9060\n",
            "Phase val\n",
            "val Loss: 0.5023 Acc: 0.5668, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2305 Acc: 0.7597, Teacher Acc (Sanity check): 0.9396\n",
            "Phase val\n",
            "val Loss: 0.3795 Acc: 0.6924, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.1975 Acc: 0.8067, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.4708 Acc: 0.6164, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2307 Acc: 0.7799, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4175 Acc: 0.6313, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2246 Acc: 0.7785, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.4299 Acc: 0.6578, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Training complete in 3m 8s\n",
            "Best val Acc: 0.692396\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6000 Acc: 0.7852\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0548 Acc: 0.6924\n",
            "\n",
            "Tested in 0m 4s Loss: 1.2667 Acc: 0.6352\n",
            "Accuracy. Train: 78.52% val: 69.24% test: 63.52%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5160 Acc: 0.4792, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.5291 Acc: 0.5449, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4267 Acc: 0.5584, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.6525 Acc: 0.4182, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3765 Acc: 0.6013, Teacher Acc (Sanity check): 0.9034\n",
            "Phase val\n",
            "val Loss: 0.3161 Acc: 0.6636, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3469 Acc: 0.6309, Teacher Acc (Sanity check): 0.9087\n",
            "Phase val\n",
            "val Loss: 0.3972 Acc: 0.6129, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3022 Acc: 0.6846, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.3879 Acc: 0.6544, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3176 Acc: 0.6993, Teacher Acc (Sanity check): 0.9275\n",
            "Phase val\n",
            "val Loss: 0.4309 Acc: 0.6187, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2843 Acc: 0.6859, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.3829 Acc: 0.5876, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3039 Acc: 0.6872, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.4068 Acc: 0.6048, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2732 Acc: 0.7074, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.4529 Acc: 0.6048, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2718 Acc: 0.7114, Teacher Acc (Sanity check): 0.9114\n",
            "Phase val\n",
            "val Loss: 0.3850 Acc: 0.5588, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2717 Acc: 0.7047, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.3536 Acc: 0.6336, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2533 Acc: 0.7503, Teacher Acc (Sanity check): 0.9074\n",
            "Phase val\n",
            "val Loss: 0.3526 Acc: 0.6694, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2600 Acc: 0.7342, Teacher Acc (Sanity check): 0.9289\n",
            "Phase val\n",
            "val Loss: 0.3488 Acc: 0.6590, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2431 Acc: 0.7463, Teacher Acc (Sanity check): 0.9342\n",
            "Phase val\n",
            "val Loss: 0.3843 Acc: 0.5864, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2536 Acc: 0.7544, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4229 Acc: 0.5783, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2241 Acc: 0.7852, Teacher Acc (Sanity check): 0.9356\n",
            "Phase val\n",
            "val Loss: 0.4015 Acc: 0.6129, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2460 Acc: 0.7530, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.4164 Acc: 0.6233, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2191 Acc: 0.7691, Teacher Acc (Sanity check): 0.9329\n",
            "Phase val\n",
            "val Loss: 0.4480 Acc: 0.6071, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2202 Acc: 0.7906, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.5038 Acc: 0.5968, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2350 Acc: 0.7732, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.4371 Acc: 0.5737, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2335 Acc: 0.7852, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.3448 Acc: 0.6532, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2142 Acc: 0.7758, Teacher Acc (Sanity check): 0.9248\n",
            "Phase val\n",
            "val Loss: 0.4394 Acc: 0.6290, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2066 Acc: 0.7893, Teacher Acc (Sanity check): 0.9315\n",
            "Phase val\n",
            "val Loss: 0.4282 Acc: 0.6221, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2248 Acc: 0.7638, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.3900 Acc: 0.6567, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2266 Acc: 0.7691, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.3810 Acc: 0.6382, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Training complete in 3m 7s\n",
            "Best val Acc: 0.669355\n",
            "\n",
            "Tested in 0m 3s Loss: 0.6245 Acc: 0.7705\n",
            "\n",
            "Tested in 0m 2s Loss: 1.0124 Acc: 0.6694\n",
            "\n",
            "Tested in 0m 4s Loss: 1.1685 Acc: 0.6188\n",
            "Accuracy. Train: 77.05% val: 66.94% test: 61.88%\n",
            "Epoch 0/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.5142 Acc: 0.4658, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.4957 Acc: 0.5230, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.4169 Acc: 0.5450, Teacher Acc (Sanity check): 0.9235\n",
            "Phase val\n",
            "val Loss: 0.4332 Acc: 0.4942, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3479 Acc: 0.6134, Teacher Acc (Sanity check): 0.9195\n",
            "Phase val\n",
            "val Loss: 0.3644 Acc: 0.5979, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3475 Acc: 0.6416, Teacher Acc (Sanity check): 0.9087\n",
            "Phase val\n",
            "val Loss: 0.3526 Acc: 0.6336, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3140 Acc: 0.6685, Teacher Acc (Sanity check): 0.9141\n",
            "Phase val\n",
            "val Loss: 0.3237 Acc: 0.6406, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.3028 Acc: 0.7087, Teacher Acc (Sanity check): 0.9181\n",
            "Phase val\n",
            "val Loss: 0.3867 Acc: 0.5461, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2744 Acc: 0.6872, Teacher Acc (Sanity check): 0.9074\n",
            "Phase val\n",
            "val Loss: 0.3796 Acc: 0.6014, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2701 Acc: 0.7181, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.4236 Acc: 0.6244, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2777 Acc: 0.7195, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.4836 Acc: 0.5530, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2748 Acc: 0.6872, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.5386 Acc: 0.5230, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2687 Acc: 0.7020, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.3840 Acc: 0.6060, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2483 Acc: 0.7409, Teacher Acc (Sanity check): 0.9369\n",
            "Phase val\n",
            "val Loss: 0.4347 Acc: 0.6452, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2523 Acc: 0.7275, Teacher Acc (Sanity check): 0.9221\n",
            "Phase val\n",
            "val Loss: 0.4586 Acc: 0.6014, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2432 Acc: 0.7638, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.4091 Acc: 0.6325, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2323 Acc: 0.7745, Teacher Acc (Sanity check): 0.9262\n",
            "Phase val\n",
            "val Loss: 0.4123 Acc: 0.6198, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2274 Acc: 0.7745, Teacher Acc (Sanity check): 0.8913\n",
            "Phase val\n",
            "val Loss: 0.3695 Acc: 0.6682, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2224 Acc: 0.7745, Teacher Acc (Sanity check): 0.9168\n",
            "Phase val\n",
            "val Loss: 0.4322 Acc: 0.6279, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2391 Acc: 0.7638, Teacher Acc (Sanity check): 0.9208\n",
            "Phase val\n",
            "val Loss: 0.5064 Acc: 0.5484, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2440 Acc: 0.7772, Teacher Acc (Sanity check): 0.9154\n",
            "Phase val\n",
            "val Loss: 0.4324 Acc: 0.6359, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2306 Acc: 0.7638, Teacher Acc (Sanity check): 0.9302\n",
            "Phase val\n",
            "val Loss: 0.5025 Acc: 0.5311, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2392 Acc: 0.7691, Teacher Acc (Sanity check): 0.9128\n",
            "Phase val\n",
            "val Loss: 0.3634 Acc: 0.6866, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2224 Acc: 0.8013, Teacher Acc (Sanity check): 0.9060\n",
            "Phase val\n",
            "val Loss: 0.3716 Acc: 0.6267, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2164 Acc: 0.8027, Teacher Acc (Sanity check): 0.8940\n",
            "Phase val\n",
            "val Loss: 0.3661 Acc: 0.6244, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2083 Acc: 0.8027, Teacher Acc (Sanity check): 0.9195\n",
            "Phase val\n",
            "val Loss: 0.3936 Acc: 0.6175, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Phase train\n",
            "train Loss: 0.2287 Acc: 0.7557, Teacher Acc (Sanity check): 0.9302\n",
            "Phase val\n",
            "val Loss: 0.5143 Acc: 0.5449, Teacher Acc (Sanity check): 0.7823\n",
            "\n",
            "Training complete in 3m 9s\n",
            "Best val Acc: 0.686636\n",
            "\n",
            "Tested in 0m 3s Loss: 0.5807 Acc: 0.7812\n",
            "\n",
            "Tested in 0m 3s Loss: 1.0392 Acc: 0.6866\n",
            "\n",
            "Tested in 0m 5s Loss: 1.1757 Acc: 0.6478\n",
            "Accuracy. Train: 78.12% val: 68.66% test: 64.78%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhpo2cq-zHMY",
        "colab_type": "code",
        "outputId": "793c36fc-9d90-46bd-cb0c-b652559d426e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Mean training HHA distilled from RGB {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "print('StDev training HHA distilled from RGB {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), train_accs)))))\n",
        "\n",
        "print('Mean validation HHA distilled from RGB {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "print('StDev validation HHA distilled from RGB {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), val_accs)))))\n",
        "\n",
        "print('Mean test HHA distilled from RGB {}%'.format(100*np.mean(list(map(lambda x: x.cpu().numpy(), test_accs)))))\n",
        "print('StDev test HHA distilled from RGB {}%'.format(100*np.std(list(map(lambda x: x.cpu().numpy(), test_accs)))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean training HHA distilled from RGB 76.42953395843506%\n",
            "StDev training HHA distilled from RGB 2.636619657278061%\n",
            "Mean validation HHA distilled from RGB 68.1797206401825%\n",
            "StDev validation HHA distilled from RGB 0.8664323948323727%\n",
            "Mean test HHA distilled from RGB 63.25141191482544%\n",
            "StDev test HHA distilled from RGB 1.4103429391980171%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZHL_0cD2RJD",
        "colab_type": "text"
      },
      "source": [
        "Experiment Name | Train Accuracy (Avg/StdDev)| Val Accuracy (Avg/StdDev)| Test accuracy (Avg/StdDev)|\n",
        "--- | --- | --- | ---\n",
        "HHA |  77.23 % / 3.36 % | 66.20 % / 1.25 % | 61.37 % / 1.64 %\n",
        "HHA-Distilled | 76.43 % / 2.64 % | 68.18% / 0.87 % | 63.25 % / 1.41 %\n",
        "\n",
        "Although it may not be clear that there is an improvement, we have seen that at least it reaches similar performance by using the output of a better performing system. Although this technique is mostly used to do some model reduction, it may also be used to transfer knowledge to different domains."
      ]
    }
  ]
}