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      "source": [
        "# Dataset available at kaggle\n",
        "\n",
        "https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset"
      ],
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      ],
      "metadata": {
        "id": "1P0cCKiP43Z8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install libraries, packages and dataset"
      ],
      "metadata": {
        "id": "TVZJB6C5vobN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WPaMVYICuTRa",
        "outputId": "0c701bb8-7f0c-4318-e4ba-8ad75621c6e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Facial-Expression-Dataset'...\n",
            "remote: Enumerating objects: 34052, done.\u001b[K\n",
            "remote: Total 34052 (delta 0), reused 0 (delta 0), pack-reused 34052\u001b[K\n",
            "Receiving objects: 100% (34052/34052), 52.31 MiB | 14.39 MiB/s, done.\n",
            "Resolving deltas: 100% (4/4), done.\n",
            "Updating files: 100% (35887/35887), done.\n",
            "Collecting git+https://github.com/albumentations-team/albumentations\n",
            "  Cloning https://github.com/albumentations-team/albumentations to /tmp/pip-req-build-0ctk0cl9\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/albumentations-team/albumentations /tmp/pip-req-build-0ctk0cl9\n",
            "  Resolved https://github.com/albumentations-team/albumentations to commit d40477d5643e5031278312cfe924d241b3ae3afd\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: albumentations\n",
            "  Building wheel for albumentations (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for albumentations: filename=albumentations-1.4.8-py3-none-any.whl size=159056 sha256=6131c758301069cc28eabe739f845da2117943f5af34b8a0523df1796c56188a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-z_axsgpj/wheels/51/4d/ab/5aafa8b980086fbc362946de7da4aa3df33aacb3da0da29b93\n",
            "Successfully built albumentations\n",
            "Installing collected packages: imageio, scikit-learn, scikit-image, albucore, albumentations\n",
            "  Attempting uninstall: imageio\n",
            "    Found existing installation: imageio 2.31.6\n",
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            "      Successfully uninstalled imageio-2.31.6\n",
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            "    Found existing installation: albumentations 1.3.1\n",
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            "      Successfully uninstalled albumentations-1.3.1\n",
            "Successfully installed albucore-0.0.10 albumentations-1.4.8 imageio-2.34.1 scikit-image-0.23.2 scikit-learn-1.5.0\n",
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            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, timm\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 timm-1.0.3\n",
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            "Collecting opencv-contrib-python\n",
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            "Installing collected packages: opencv-contrib-python\n",
            "  Attempting uninstall: opencv-contrib-python\n",
            "    Found existing installation: opencv-contrib-python 4.8.0.76\n",
            "    Uninstalling opencv-contrib-python-4.8.0.76:\n",
            "      Successfully uninstalled opencv-contrib-python-4.8.0.76\n",
            "Successfully installed opencv-contrib-python-4.10.0.82\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/parth1620/Facial-Expression-Dataset.git\n",
        "!pip install -U git+https://github.com/albumentations-team/albumentations\n",
        "!pip install timm\n",
        "!pip install --upgrade opencv-contrib-python"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "xoVVdBpoweEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch"
      ],
      "metadata": {
        "id": "creZrxH0vv20"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Configurations"
      ],
      "metadata": {
        "id": "p5604GKdwtAr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_IMG_FOLDER_PATH = '/content/Facial-Expression-Dataset/train/'\n",
        "VALID_IMG_FOLDER_PATH = '/content/Facial-Expression-Dataset/validation/'\n",
        "\n",
        "LR = 0.001\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 20\n",
        "\n",
        "DEVICE = \"cuda\"\n",
        "MODEL_NAME = 'efficient_b3'"
      ],
      "metadata": {
        "id": "dd6v8vyGwp3F"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset"
      ],
      "metadata": {
        "id": "wn5jWAF9xDUY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision import transforms as T"
      ],
      "metadata": {
        "id": "uvujfkVHxJ0V"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_augs = T.Compose([\n",
        "    T.RandomHorizontalFlip(p = 0.5),\n",
        "    T.RandomRotation(degrees=(-20, +20)),\n",
        "    T.ColorJitter((0.8, 1.2)),  # Adjusting contrast with probability 0.5, factor between 0.8 and 1.2\n",
        "    T.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)), # Using GaussianBlur\n",
        "    T.ToTensor() # PIL / numpy arr -> torch tensor -> (h, w, c) -> (c, h, w)\n",
        "])\n",
        "\n",
        "valid_augs = T.Compose([\n",
        "    T.ToTensor()\n",
        "])"
      ],
      "metadata": {
        "id": "Jqf-Ktqsx9WL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainset = ImageFolder(TRAIN_IMG_FOLDER_PATH, transform = train_augs)\n",
        "validset = ImageFolder(VALID_IMG_FOLDER_PATH, transform = valid_augs)"
      ],
      "metadata": {
        "id": "oeU1NfmYxC7J"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total no. of examples in trainset : {len(trainset)}\")\n",
        "print(f\"Total no. of examples in validset : {len(validset)}\")"
      ],
      "metadata": {
        "id": "eznRIKAzzuCj",
        "outputId": "e9abc9d6-93e6-47ab-825a-ebbe660b8b12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no. of examples in trainset : 28821\n",
            "Total no. of examples in validset : 7066\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(trainset.class_to_idx)"
      ],
      "metadata": {
        "id": "NtoA4SRny_vz",
        "outputId": "0fb78cbb-1329-4fc1-e591-1e3b899ec5ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = trainset[500]\n",
        "\n",
        "plt.imshow(image.permute(1, 2, 0)) # (h, w, c)\n",
        "plt.title(label)"
      ],
      "metadata": {
        "id": "0-b1SLqNw91J",
        "outputId": "d2999b49-e4f0-4297-8052-4cc9d5b56fbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '0')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = validset[4000]\n",
        "\n",
        "plt.imshow(image.permute(1, 2, 0)) # (h, w, c)\n",
        "plt.title(label)"
      ],
      "metadata": {
        "id": "kjVHfYb6qZiF",
        "outputId": "0b1d5720-d0c1-4c5f-85c2-3e99ad111415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '4')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset into Batches"
      ],
      "metadata": {
        "id": "UjVmykAGy0Sh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader"
      ],
      "metadata": {
        "id": "egpuikhHymP7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainloader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "validloader = DataLoader(validset, batch_size=BATCH_SIZE)"
      ],
      "metadata": {
        "id": "Pea8G9-HzhuZ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total no. of batches in trainloader : {len(trainloader)}\")\n",
        "print(f\"Total no. of batches in validloader : {len(validloader)}\")"
      ],
      "metadata": {
        "id": "mjf9oeOBzihh",
        "outputId": "fde6d35b-f0dd-477f-ddbc-52e7e010d26e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no. of batches in trainloader : 901\n",
            "Total no. of batches in validloader : 221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for images, labels in trainloader:\n",
        "    break\n",
        "\n",
        "print(f\"One image batch shape : {images.shape}\")\n",
        "print(f\"One label batch shape : {labels.shape}\")"
      ],
      "metadata": {
        "id": "vtVSVVj-zp1C",
        "outputId": "eb26def4-ac12-4e61-af01-59de01054547",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One image batch shape : torch.Size([32, 3, 48, 48])\n",
            "One label batch shape : torch.Size([32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Model"
      ],
      "metadata": {
        "id": "GQvgvbKu0TUG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import timm\n",
        "from torch import nn"
      ],
      "metadata": {
        "id": "iqwWf5-O0SWP"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class FaceModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(FaceModel, self).__init__()\n",
        "        self.eff_net = timm.create_model('efficientnet_b3', pretrained = True, num_classes = 7)\n",
        "    def forward(self, images, labels = None):\n",
        "       logits = self.eff_net(images)\n",
        "       if labels != None:\n",
        "         loss = nn.CrossEntropyLoss()(logits, labels)\n",
        "         return logits, loss\n",
        "       return logits\n"
      ],
      "metadata": {
        "id": "U6uJZmUw0XJX"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = FaceModel()\n",
        "model.to(DEVICE)"
      ],
      "metadata": {
        "id": "hgp5OjEO0_2w",
        "outputId": "3b606bcb-dd04-4462-a3cd-99a1a46869d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d9e043c7621e45938922713dee48580f",
            "eeb1b8bb1e2640ae8dd864bbfed550eb",
            "9b236ff857384f3b924a56b71226a351",
            "533758a2c0ac4793be577b0bd0b91c45",
            "8b94fb095a2742ec8342ff8250a1a9b9",
            "f42bafc6d7d24092960e77fefc6f45b9",
            "6b1dbecb618e4402aed7ae7f96881622",
            "4cc94303378343c7b030e76f0621bdcd",
            "5030b43a1454499fa61b07ab4bcef8f2",
            "63c439f821f44f61beee140d158a4690",
            "87bfcdcf88cb452d8d022b44b7209d03"
          ]
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/49.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9e043c7621e45938922713dee48580f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FaceModel(\n",
              "  (eff_net): EfficientNet(\n",
              "    (conv_stem): Conv2d(3, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (bn1): BatchNormAct2d(\n",
              "      40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (drop): Identity()\n",
              "      (act): SiLU(inplace=True)\n",
              "    )\n",
              "    (blocks): Sequential(\n",
              "      (0): Sequential(\n",
              "        (0): DepthwiseSeparableConv(\n",
              "          (conv_dw): Conv2d(40, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=40, bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(40, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(10, 40, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pw): Conv2d(40, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): DepthwiseSeparableConv(\n",
              "          (conv_dw): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(24, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(6, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pw): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (2): InvertedResidual(\n",
              "          (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (2): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(192, 192, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=192, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=288, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (2): InvertedResidual(\n",
              "          (conv_pw): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=288, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (3): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(288, 288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=288, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (2): InvertedResidual(\n",
              "          (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (3): InvertedResidual(\n",
              "          (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (4): InvertedResidual(\n",
              "          (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (4): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=576, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(576, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=816, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (2): InvertedResidual(\n",
              "          (conv_pw): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=816, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (3): InvertedResidual(\n",
              "          (conv_pw): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=816, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (4): InvertedResidual(\n",
              "          (conv_pw): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=816, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (5): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(816, 816, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=816, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            816, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(816, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (2): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (3): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (4): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (5): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "      (6): Sequential(\n",
              "        (0): InvertedResidual(\n",
              "          (conv_pw): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(1392, 1392, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1392, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            1392, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(1392, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "        (1): InvertedResidual(\n",
              "          (conv_pw): Conv2d(384, 2304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): BatchNormAct2d(\n",
              "            2304, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (conv_dw): Conv2d(2304, 2304, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2304, bias=False)\n",
              "          (bn2): BatchNormAct2d(\n",
              "            2304, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (se): SqueezeExcite(\n",
              "            (conv_reduce): Conv2d(2304, 96, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (act1): SiLU(inplace=True)\n",
              "            (conv_expand): Conv2d(96, 2304, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (gate): Sigmoid()\n",
              "          )\n",
              "          (conv_pwl): Conv2d(2304, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): BatchNormAct2d(\n",
              "            384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "            (drop): Identity()\n",
              "            (act): Identity()\n",
              "          )\n",
              "          (drop_path): Identity()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (conv_head): Conv2d(384, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn2): BatchNormAct2d(\n",
              "      1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (drop): Identity()\n",
              "      (act): SiLU(inplace=True)\n",
              "    )\n",
              "    (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "    (classifier): Linear(in_features=1536, out_features=7, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Train and Eval Function"
      ],
      "metadata": {
        "id": "GzDb6TdaCpF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "eLQVQA0PVjX8"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def multiclass_accuracy(y_pred,y_true):\n",
        "    top_p,top_class = y_pred.topk(1,dim = 1)\n",
        "    equals = top_class == y_true.view(*top_class.shape)\n",
        "    return torch.mean(equals.type(torch.FloatTensor))"
      ],
      "metadata": {
        "id": "esp-tSrxTiPX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_fn(model, dataloader, optimizer, current_epo):\n",
        "   model.train()\n",
        "   total_loss = 0.0\n",
        "   total_acc =0.0\n",
        "   tk = tqdm(dataloader, desc =\"EPOCH\" + \"[TRAIN]\" + str(current_epo + 1) +\"/\"+ str(EPOCHS))\n",
        "\n",
        "\n",
        "   for t, data in enumerate(tk):\n",
        "       images, labels = data\n",
        "       images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
        "\n",
        "       optimizer.zero_grad()\n",
        "       logits, loss = model(images, labels)\n",
        "       loss.backward()\n",
        "       optimizer.step()\n",
        "\n",
        "       total_loss += loss.item()\n",
        "       total_acc += multiclass_accuracy(logits, labels)\n",
        "       tk.set_postfix({'loss':'%6f' %float(total_loss / (t+1)), 'acc' :'%6f' %float(total_acc / (t+1)),})\n",
        "   return total_loss / len(dataloader), total_acc / len(dataloader)"
      ],
      "metadata": {
        "id": "QBim_pZxColX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_fn(model, dataloader, current_epo):\n",
        "   model.eval()\n",
        "   total_loss = 0.0\n",
        "   total_acc =0.0\n",
        "   tk = tqdm(dataloader, desc =\"EPOCH\" + \"[VALID]\" + str(current_epo + 1) +\"/\"+ str(EPOCHS))\n",
        "\n",
        "\n",
        "   for t, data in enumerate(tk):\n",
        "       images, labels = data\n",
        "       images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
        "\n",
        "       logits, loss = model(images, labels)\n",
        "\n",
        "       total_loss += loss.item()\n",
        "       total_acc += multiclass_accuracy(logits, labels)\n",
        "       tk.set_postfix({'loss':'%6f' %float(total_loss / (t+1)), 'acc' :'%6f' %float(total_acc / (t+1)),})\n",
        "   return total_loss / len(dataloader), total_acc / len(dataloader)"
      ],
      "metadata": {
        "id": "T3eGYNqR1FeL"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Training Loop"
      ],
      "metadata": {
        "id": "f2YsWQvsXiKG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr = LR)"
      ],
      "metadata": {
        "id": "mbKd9D7pWeM_"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "best_valid_loss = np.Inf  # Initialized with positive infinity\n",
        "training_losses = []\n",
        "validation_losses = []\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    train_loss, train_acc = train_fn(model, trainloader, optimizer, epoch)\n",
        "    valid_loss, valid_acc = eval_fn(model, validloader, epoch)\n",
        "\n",
        "    training_losses.append(train_loss)\n",
        "    validation_losses.append(valid_loss)\n",
        "\n",
        "    if valid_loss < best_valid_loss:\n",
        "        torch.save(model.state_dict(), 'best-weights.pt')\n",
        "        print('SAVED-BEST-WEIGHTS')\n",
        "        best_valid_loss = valid_loss\n",
        "\n",
        "    # Early Stopping\n",
        "    patience = 10  # Number of epochs with no improvement to wait before stopping\n",
        "    if epoch > patience and (np.argmin(validation_losses) >= (epoch - patience)):\n",
        "        print('Early Stopping: Validation loss has not improved in the last {} epochs.'.format(patience))\n",
        "        break\n",
        "\n",
        "# Plotting Loss Convergence Graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(training_losses, label='Training Loss')\n",
        "plt.plot(validation_losses, label='Validation Loss')\n",
        "plt.title('Loss Convergence')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ba0i4Vl8f0-1",
        "outputId": "4d6b5a33-4932-46ad-c0a1-ad3dfb042c77"
      },
      "execution_count": 24,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]1/20:   0%|          | 0/901 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:744: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
            "EPOCH[TRAIN]1/20: 100%|██████████| 901/901 [02:06<00:00,  7.14it/s, loss=1.875931, acc=0.308133]\n",
            "EPOCH[VALID]1/20: 100%|██████████| 221/221 [00:09<00:00, 22.96it/s, loss=1.621720, acc=0.358358]\n"
          ]
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        {
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          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
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            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]2/20: 100%|██████████| 901/901 [01:52<00:00,  8.03it/s, loss=1.533500, acc=0.409296]\n",
            "EPOCH[VALID]2/20: 100%|██████████| 221/221 [00:09<00:00, 24.32it/s, loss=1.455852, acc=0.444777]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]3/20: 100%|██████████| 901/901 [01:49<00:00,  8.24it/s, loss=1.415448, acc=0.458162]\n",
            "EPOCH[VALID]3/20: 100%|██████████| 221/221 [00:09<00:00, 24.11it/s, loss=1.551274, acc=0.401257]\n",
            "EPOCH[TRAIN]4/20: 100%|██████████| 901/901 [01:53<00:00,  7.96it/s, loss=1.367003, acc=0.476040]\n",
            "EPOCH[VALID]4/20: 100%|██████████| 221/221 [00:08<00:00, 24.78it/s, loss=1.507857, acc=0.454675]\n",
            "EPOCH[TRAIN]5/20: 100%|██████████| 901/901 [01:48<00:00,  8.28it/s, loss=1.325028, acc=0.496383]\n",
            "EPOCH[VALID]5/20: 100%|██████████| 221/221 [00:08<00:00, 24.80it/s, loss=1.276991, acc=0.519677]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]6/20: 100%|██████████| 901/901 [01:47<00:00,  8.39it/s, loss=1.293597, acc=0.510349]\n",
            "EPOCH[VALID]6/20: 100%|██████████| 221/221 [00:07<00:00, 28.65it/s, loss=1.251420, acc=0.531881]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]7/20: 100%|██████████| 901/901 [01:45<00:00,  8.51it/s, loss=1.243284, acc=0.526731]\n",
            "EPOCH[VALID]7/20: 100%|██████████| 221/221 [00:08<00:00, 26.20it/s, loss=1.188073, acc=0.559172]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]8/20: 100%|██████████| 901/901 [01:50<00:00,  8.15it/s, loss=1.217194, acc=0.539600]\n",
            "EPOCH[VALID]8/20: 100%|██████████| 221/221 [00:08<00:00, 25.34it/s, loss=1.225151, acc=0.568743]\n",
            "EPOCH[TRAIN]9/20: 100%|██████████| 901/901 [01:51<00:00,  8.11it/s, loss=1.199482, acc=0.546450]\n",
            "EPOCH[VALID]9/20: 100%|██████████| 221/221 [00:08<00:00, 26.66it/s, loss=1.275905, acc=0.562000]\n",
            "EPOCH[TRAIN]10/20: 100%|██████████| 901/901 [01:46<00:00,  8.47it/s, loss=1.191300, acc=0.550683]\n",
            "EPOCH[VALID]10/20: 100%|██████████| 221/221 [00:09<00:00, 22.47it/s, loss=1.137259, acc=0.573616]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "EPOCH[TRAIN]11/20: 100%|██████████| 901/901 [01:48<00:00,  8.29it/s, loss=1.154316, acc=0.564173]\n",
            "EPOCH[VALID]11/20: 100%|██████████| 221/221 [00:09<00:00, 24.52it/s, loss=1.125728, acc=0.571049]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SAVED-BEST-WEIGHTS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "EPOCH[TRAIN]12/20: 100%|██████████| 901/901 [01:49<00:00,  8.20it/s, loss=1.126306, acc=0.579160]\n",
            "EPOCH[VALID]12/20: 100%|██████████| 221/221 [00:09<00:00, 23.69it/s, loss=1.102269, acc=0.594588]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SAVED-BEST-WEIGHTS\n",
            "Early Stopping: Validation loss has not improved in the last 10 epochs.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "cjiklrErXkZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def view_classify(img, ps):\n",
        "\n",
        "    classes = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
        "\n",
        "    ps = ps.data.cpu().numpy().squeeze()\n",
        "    img = img.numpy().transpose(1,2,0)\n",
        "\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(5,9), ncols=2)\n",
        "    ax1.imshow(img)\n",
        "    ax1.axis('off')\n",
        "    ax2.barh(classes, ps)\n",
        "    ax2.set_aspect(0.1)\n",
        "    ax2.set_yticks(classes)\n",
        "    ax2.set_yticklabels(classes)\n",
        "    ax2.set_title('Class Probability')\n",
        "    ax2.set_xlim(0, 1.1)\n",
        "    plt.tight_layout()\n",
        "    return None"
      ],
      "metadata": {
        "id": "8iozgi4xhLNg"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = validset[100]\n",
        "image = image.unsqueeze(0)\n",
        "\n",
        "logits = model(image.to(DEVICE))\n",
        "probs = nn.Softmax(dim = 1)(logits)\n",
        "\n",
        "view_classify(image.squeeze(), probs)"
      ],
      "metadata": {
        "id": "y_QZvnUGXmOf",
        "outputId": "f1dc146b-322d-4343-fddd-7d75841b06f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = validset[106]\n",
        "image = image.unsqueeze(0)\n",
        "\n",
        "logits = model(image.to(DEVICE))\n",
        "probs = nn.Softmax(dim = 1)(logits)\n",
        "\n",
        "view_classify(image.squeeze(), probs)"
      ],
      "metadata": {
        "id": "r3cwa6LErKgx",
        "outputId": "5bcf26ca-1075-48ec-d122-1c39876ba759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zzGjz5WGdXru"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}