efficientnet_pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "name": "efficientnet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULhOFl0HkA5J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -rf data models fashion.tar.gz data_preprocess.py utils.py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4aCBrbAmR5t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -rf data models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWokEyDP5zMp",
        "colab_type": "code",
        "outputId": "b1f89b59-61ba-4d69-c598-a127e0ce9208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ak3r6ds9bQlP",
        "colab_type": "code",
        "outputId": "aab68889-77bb-4f37-95ea-544ba6373cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# using csv file\n",
        "# !wget -O filter1.tar.gz https://husteduvn-my.sharepoint.com/:u:/g/personal/thanh_lt163705_sis_hust_edu_vn/EYZpSIV_Oz1IhXEMjbCXFqIBYZ1nP7TPxK71o8L6hW5b6w?download=1\n",
        "#!tar -zxvf filter1.tar.gz\n",
        "\n",
        "# use train, test folder\n",
        "# !wget -O data.tar.gz https://husteduvn-my.sharepoint.com/:u:/g/personal/thanh_lt163705_sis_hust_edu_vn/ER-r_LgP529LkkK0FqGwbBMBm76hhw3Z8s__HlOJPAx3Hg?download=1\n",
        "# !tar -zxvf data.tar.gz\n",
        "\n",
        "!wget -O efficientnet_pretrained.tar.gz https://husteduvn-my.sharepoint.com/:u:/g/personal/thanh_lt163705_sis_hust_edu_vn/EZ0rYiGAZX1MmJWaqBr8yv0B62gWwCOZVti1uLL5CqYJRQ?download=1\n",
        "!tar -zxvf efficientnet_pretrained.tar.gz\n",
        "\n",
        "!wget -O deepfashion.tar.gz https://husteduvn-my.sharepoint.com/:u:/g/personal/thanh_lt163705_sis_hust_edu_vn/EQ_uFyOgyk5JvzcjFrlOo6ABEkH1vxHNGH5ACnDIApS7Yw?download=1\n",
        "!tar -zxvf deepfashion.tar.gz\n",
        "!unzip img.zip\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOcqO6onbwok",
        "colab_type": "code",
        "outputId": "3668517e-5d64-4a75-8520-1b4bc908d756",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        }
      },
      "source": [
        "!wget -O efficientnet_b5.pth https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth\n",
        "!wget -O efficientnet_b4.pth https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth\n",
        "!wget -O efficientnet_b0.pth https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-16 06:17:06--  https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/189350661/a1dff680-5b42-11ea-947a-e8793ee06c61?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200516%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200516T061707Z&X-Amz-Expires=300&X-Amz-Signature=2137a0bbfa7ef525a5c9584353befa9df9bd825baeb68d5f823bbdb11a20fa49&X-Amz-SignedHeaders=host&actor_id=0&repo_id=189350661&response-content-disposition=attachment%3B%20filename%3Defficientnet-b5-b6417697.pth&response-content-type=application%2Foctet-stream [following]\n",
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            "\n"
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rb2kPv3lcg1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !mkdir data/shopee/train_images/full\n",
        "# !mv data/shopee/train_images/*.jpg data/shopee/train_images/full"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXR7b4IDcENS",
        "colab_type": "code",
        "outputId": "3b7fecf1-9538-4c43-912e-cd65ffb5ec1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "source": [
        "!pip install efficientnet_pytorch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet_pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from efficientnet_pytorch) (1.5.0+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->efficientnet_pytorch) (1.18.4)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->efficientnet_pytorch) (0.16.0)\n",
            "Building wheels for collected packages: efficientnet-pytorch\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp36-none-any.whl size=12422 sha256=afcbf8a38eee33b82e237a65b43e510e3d26ab7a196869cb7fa2e67e1bc28967\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.6.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEucLbfma_S7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "import fastai\n",
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "from fastai.callbacks import *\n",
        "from fastai.basic_train import *\n",
        "from fastai.vision.learner import *\n",
        "from efficientnet_pytorch import EfficientNet\n",
        "from efficientnet_pytorch.utils import MemoryEfficientSwish\n",
        "warnings.filterwarnings('always')\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "elf-j8Dha_S-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "seed_everything(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "7cSXk_P3a_TB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = Path('data/fashion_data')\n",
        "# images = pd.read_csv(path/'images.csv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "jGSqnx1fa_TG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# images.label.value_counts().plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "CE4QkWc2a_TK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# data = ImageDataBunch.from_df(\n",
        "#     df=images,\n",
        "#     label_col='label',\n",
        "#     fn_col='path',\n",
        "#     path=path,\n",
        "#     folder='merge',\n",
        "#     # suffix='.jpg',\n",
        "#     valid_pct=0.1,\n",
        "#     ds_tfms=get_transforms(flip_vert=True, max_warp=0.1, max_zoom=1.15, max_rotate=45.),\n",
        "#     size=224,\n",
        "#     bs=32,\n",
        "#     num_workers=os.cpu_count()\n",
        "# ).normalize()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GE3rhR4AjR3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# data from folder\n",
        "# data = ImageDataBunch.from_folder(\n",
        "#     path=path,\n",
        "#     train='train',\n",
        "#     test='test',\n",
        "#     valid_pct=0.3,\n",
        "#     seed=42,\n",
        "#     bs=8,\n",
        "#     ds_tfms=get_transforms(flip_vert=True, max_warp=0.1, max_zoom=1.15, max_rotate=45.),\n",
        "#     size=224,\n",
        "#     num_workers=os.cpu_count(),\n",
        "# ).normalize(imagenet_stats)\n",
        "\n",
        "# data_test = ImageDataBunch.from_folder(\n",
        "#     path=path,\n",
        "#     train='test',\n",
        "#     test='train',\n",
        "#     valid_pct=0,\n",
        "#     seed=42,\n",
        "#     bs=64,\n",
        "#     ds_tfms=get_transforms(flip_vert=True, max_warp=0.1, max_zoom=1.15, max_rotate=45.),\n",
        "#     size=224,\n",
        "#     num_workers=os.cpu_count(),\n",
        "#     classes=data.classes,\n",
        "# ).normalize(imagenet_stats)\n",
        "# data_test.train_dl = data_test.train_dl.new(shuffle=False, drop_last=False)\n",
        "# data.test_dl = data_test.train_dl"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ij2GmkLUt3D6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('csv/train_eval.csv', sep='\\t')\n",
        "test_df = pd.read_csv('csv/test.csv', sep='\\t')\n",
        "src = (\n",
        "    ImageList.from_df(\n",
        "        train_df,\n",
        "        path='.',\n",
        "        cols='image_name',)\n",
        "    .split_from_df(col='is_valid')\n",
        "    .label_from_df(cols='category')\n",
        ")\n",
        "data = (\n",
        "    src.transform(get_transforms(flip_vert=True, max_warp=0.1, max_zoom=1.15, max_rotate=45.), size=224)\n",
        "    .databunch(num_workers=os.cpu_count(), bs=32,)\n",
        "    .normalize(imagenet_stats)\n",
        ")\n",
        "test = ImageDataBunch.from_df(\n",
        "    df=test_df,\n",
        "    label_col='category',\n",
        "    fn_col='image_name',\n",
        "    path='.',\n",
        "    folder='.',\n",
        "    valid_pct=0.,\n",
        "    ds_tfms=get_transforms(flip_vert=True, max_warp=0.1, max_zoom=1.15, max_rotate=45.),\n",
        "    size=224,\n",
        "    bs=64,\n",
        "    num_workers=os.cpu_count(),\n",
        "    seed=42,\n",
        ").normalize(imagenet_stats)\n",
        "data.test_dl = test.train_dl\n",
        "\n",
        "assert data.classes == test.classes, 'not same classes'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK7rj6FOHcEc",
        "colab_type": "code",
        "outputId": "4f2667f8-dc73-40e2-8e22-6f95346f8736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        }
      },
      "source": [
        "data.c, len(data.classes), data.classes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,\n",
              " 46,\n",
              " ['Anorak',\n",
              "  'Blazer',\n",
              "  'Blouse',\n",
              "  'Bomber',\n",
              "  'Button-Down',\n",
              "  'Caftan',\n",
              "  'Capris',\n",
              "  'Cardigan',\n",
              "  'Chinos',\n",
              "  'Coat',\n",
              "  'Coverup',\n",
              "  'Culottes',\n",
              "  'Cutoffs',\n",
              "  'Dress',\n",
              "  'Flannel',\n",
              "  'Gauchos',\n",
              "  'Halter',\n",
              "  'Henley',\n",
              "  'Hoodie',\n",
              "  'Jacket',\n",
              "  'Jeans',\n",
              "  'Jeggings',\n",
              "  'Jersey',\n",
              "  'Jodhpurs',\n",
              "  'Joggers',\n",
              "  'Jumpsuit',\n",
              "  'Kaftan',\n",
              "  'Kimono',\n",
              "  'Leggings',\n",
              "  'Onesie',\n",
              "  'Parka',\n",
              "  'Peacoat',\n",
              "  'Poncho',\n",
              "  'Robe',\n",
              "  'Romper',\n",
              "  'Sarong',\n",
              "  'Shorts',\n",
              "  'Skirt',\n",
              "  'Sweater',\n",
              "  'Sweatpants',\n",
              "  'Sweatshorts',\n",
              "  'Tank',\n",
              "  'Tee',\n",
              "  'Top',\n",
              "  'Trunks',\n",
              "  'Turtleneck'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3f41WHD5azwN",
        "colab_type": "code",
        "outputId": "ed3d233d-0811-4d4f-fe02-6455dc2203f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "data.show_batch(rows=3, figsize=(7,6))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Icf8CotCN_lb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_model_path = Path('drive/My Drive/colab_models/efficientnet')\n",
        "# data.save('data.pkl')\n",
        "# data_test.save('data_test.pkl')\n",
        "\n",
        "# data = load_data(train_model_path, 'data.pkl')\n",
        "# data_test = load_data(train_model_path, 'data_test.pkl')\n",
        "# data_test.train_dl = data_test.train_dl.new(shuffle=False, drop_last=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WrnEVUHOtXv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !cp data/fashion_data/data.pkl data/fashion_data/data_test.pkl drive/My\\ Drive/colab_models/efficientnet\n",
        "# !cp  drive/My\\ Drive/colab_models/efficientnet/*.pkl data/fashion_data/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "rbDdZMNva_TT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_efficientnet(path, model_name='efficientnet-b7', pretrained=True, num_classes=1000):\n",
        "    assert model_name in [f'efficientnet-b{i}' for i in range(8)]\n",
        "    model = EfficientNet.from_name(model_name, override_params={'num_classes':num_classes})\n",
        "\n",
        "    if pretrained:\n",
        "        model_state = torch.load(path)\n",
        "        # load original weights apart from its head\n",
        "        if '_fc.weight' in model_state.keys():\n",
        "            model_state.pop('_fc.weight')\n",
        "            model_state.pop('_fc.bias')\n",
        "            res = model.load_state_dict(model_state, strict=False)\n",
        "            assert str(res.missing_keys) == str(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'\n",
        "        else:\n",
        "            # A basic remapping is required\n",
        "            from collections import OrderedDict\n",
        "            mapping = { i:o for i,o in zip(model_state.keys(), model.state_dict().keys()) }\n",
        "            mapped_model_state = OrderedDict([\n",
        "                (mapping[k], v) for k,v in model_state.items() if not mapping[k].startswith('_fc')\n",
        "            ])\n",
        "            res = model.load_state_dict(mapped_model_state, strict=False)\n",
        "            print(res)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXcueSzoaXqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class EfficientNetClassification(nn.Module):\n",
        "    def __init__(\n",
        "            self,\n",
        "            model_name='effiecientnet-b5',\n",
        "            model_path=None,\n",
        "            pretrained=True,\n",
        "            fcs=(512, 256),\n",
        "            num_classes=1000,\n",
        "            p_dropout=0.5,\n",
        "    ):\n",
        "        super(EfficientNetClassification, self).__init__()\n",
        "\n",
        "        assert model_path is not None or pretrained is False, 'pretrained model require model_path'\n",
        "        self.efficient = load_efficientnet(\n",
        "            path=model_path,\n",
        "            model_name=model_name,\n",
        "            pretrained=pretrained,\n",
        "            num_classes=20,\n",
        "        )\n",
        "        \n",
        "        self._avg_pooling = nn.AdaptiveAvgPool2d(1)\n",
        "\n",
        "        self._mlp = self._get_mlp(\n",
        "            input_size=self.efficient._fc.in_features,\n",
        "            hidden_sizes=fcs,\n",
        "            num_classes=num_classes,\n",
        "            p_dropout=p_dropout,\n",
        "        )\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        bs = inputs.size(0)\n",
        "        x = self.efficient.extract_features(inputs)\n",
        "        x = self._avg_pooling(x)\n",
        "        x = x.view(bs, -1)\n",
        "        return self._mlp(x)\n",
        "\n",
        "\n",
        "    def extract_features(self, inputs):\n",
        "        return self.efficient.extract_features(inputs)\n",
        "\n",
        "    @staticmethod\n",
        "    def _get_mlp(input_size, hidden_sizes, num_classes, p_dropout):\n",
        "        layer_sizes = [input_size, *hidden_sizes, num_classes]\n",
        "\n",
        "        layers = []\n",
        "        for i in range(len(layer_sizes) - 1):\n",
        "            if i != len(layer_sizes) - 1:\n",
        "                layers.extend([\n",
        "                    nn.Dropout(p=p_dropout),\n",
        "                    nn.Linear(layer_sizes[i], layer_sizes[i + 1]),\n",
        "                    # nn.Sigmoid(),\n",
        "                    MemoryEfficientSwish(),\n",
        "                ])\n",
        "            else:\n",
        "                layers.extend([\n",
        "                    nn.Dropout(p=p_dropout),\n",
        "                    nn.Linear(layer_sizes[i], layer_sizes[i + 1]),\n",
        "                ])\n",
        "\n",
        "        return nn.Sequential(*layers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "EEZtqbW6a_TW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model_path = 'models/pretrained_efficientnet/efficientnet-b5-586e6cc6.pth'\n",
        "model_path = 'efficientnet_b4.pth'\n",
        "model = load_efficientnet(\n",
        "    path=model_path,\n",
        "    model_name='efficientnet-b4',\n",
        "    pretrained=True,\n",
        "    num_classes=data.c,\n",
        ")\n",
        "\n",
        "# model = EfficientNetClassification(\n",
        "#     model_name='efficientnet-b0',\n",
        "#     model_path=model_path,\n",
        "#     pretrained=True,\n",
        "#     fcs=(512, 256),\n",
        "#     num_classes=data.c,\n",
        "#     p_dropout=0.2,\n",
        "# )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "ebfFV0joa_TY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FocalLoss(nn.Module):\n",
        "    def __init__(self, gamma=3., reduction='mean'):\n",
        "        super().__init__()\n",
        "        self.gamma = gamma\n",
        "        self.reduction = reduction\n",
        "\n",
        "    def forward(self, inputs, targets):\n",
        "        ce_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)\n",
        "        pt = torch.exp(-ce_loss)\n",
        "        f_loss = ((1 - pt)**self.gamma) * ce_loss\n",
        "        if self.reduction == 'sum':\n",
        "            return f_loss.sum()\n",
        "        elif self.reduction == 'mean':\n",
        "            return f_loss.mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "8b-8OMDya_Ta",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_3_accuracy = partial(top_k_accuracy, k=3)\n",
        "top_3_accuracy.__name__ = 'top_3_accuracy'\n",
        "top_5_accuracy = partial(top_k_accuracy, k=5)\n",
        "top_5_accuracy.__name__ = 'top_5_accuracy'\n",
        "learn = Learner(\n",
        "    data=data,\n",
        "    model=model,\n",
        "    path=train_model_path,\n",
        "    loss_func=FocalLoss(),\n",
        "    metrics=[\n",
        "        accuracy,\n",
        "        top_3_accuracy,\n",
        "        top_5_accuracy,\n",
        "    ],\n",
        "    callback_fns=[\n",
        "        BnFreeze,\n",
        "        # OverSamplingCallback,\n",
        "        # partial(GradientClipping, clip=0.2),\n",
        "        # partial(SaveModelCallback, monitor='kappa_score', name='best_kappa')\n",
        "    ]\n",
        ")\n",
        "# learn.model_dir = train_model_path\n",
        "learn.split(lambda m: (model._conv_head,) )\n",
        "learn.freeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "azaM0FjVa_Td",
        "colab_type": "code",
        "outputId": "d70ff0aa-1650-4bb6-f754-24ceeb918c20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "learn.freeze()\n",
        "learn.lr_find(start_lr=1e-5, end_lr=1e1, wd=5e-3)\n",
        "learn.recorder.plot(suggestion=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='84' class='' max='6538' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      1.28% [84/6538 00:35<45:51 10.2047]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
            "Min numerical gradient: 5.01E-03\n",
            "Min loss divided by 10: 2.29E-03\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "hBh1tFWca_Tp",
        "colab_type": "code",
        "outputId": "42d1f53e-cc0d-4156-93cc-bad7d0c157fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "learn.fit_one_cycle(50, max_lr=5e-3, pct_start=0.1, div_factor=10, final_div=30, wd=5e-3, moms=(0.9, 0.8),\n",
        "                    callbacks=[SaveModelCallback(learn, monitor='accuracy', every='improvement', name='best_stage1'),\n",
        "                               EarlyStoppingCallback(learn, monitor='valid_loss', min_delta=0.01, patience=10)])\n",
        "# learn.save('stage-1')\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='6' class='' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      12.00% [6/50 4:58:34<36:29:30]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.070726</td>\n",
              "      <td>0.930307</td>\n",
              "      <td>0.581375</td>\n",
              "      <td>0.810950</td>\n",
              "      <td>0.891750</td>\n",
              "      <td>49:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.024246</td>\n",
              "      <td>0.849626</td>\n",
              "      <td>0.603550</td>\n",
              "      <td>0.827175</td>\n",
              "      <td>0.904075</td>\n",
              "      <td>49:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.945476</td>\n",
              "      <td>0.867975</td>\n",
              "      <td>0.594575</td>\n",
              "      <td>0.828700</td>\n",
              "      <td>0.905500</td>\n",
              "      <td>49:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.977344</td>\n",
              "      <td>0.885615</td>\n",
              "      <td>0.593425</td>\n",
              "      <td>0.822025</td>\n",
              "      <td>0.898750</td>\n",
              "      <td>49:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.955341</td>\n",
              "      <td>0.897551</td>\n",
              "      <td>0.590800</td>\n",
              "      <td>0.818575</td>\n",
              "      <td>0.899250</td>\n",
              "      <td>49:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.924101</td>\n",
              "      <td>0.946100</td>\n",
              "      <td>0.560725</td>\n",
              "      <td>0.797825</td>\n",
              "      <td>0.884525</td>\n",
              "      <td>51:18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='3223' class='' max='6538' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      49.30% [3223/6538 23:00<23:40 0.9621]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.581375002861023.\n",
            "Better model found at epoch 1 with accuracy value: 0.6035500168800354.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcg40qXtPHw-",
        "colab_type": "code",
        "outputId": "3d82fe04-d61e-4622-f9ba-fb6fe3255652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "learn.load('best_stage1')\n",
        "learn.validate(data.valid_dl), learn.validate(learn.data.test_dl)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([1.2282553, tensor(0.4882), tensor(0.7729), tensor(0.8746)],\n",
              " [1.5228703, tensor(0.4223), tensor(0.6926), tensor(0.8233)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tM9wiq2lLUP",
        "colab_type": "code",
        "outputId": "bbfafb4e-5e63-4e8e-b546-a9db88234fd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "# unfreeze and search appropriate learning rate for full training\n",
        "learn.load('best_stage1_bs8')\n",
        "learn.unfreeze()\n",
        "learn.lr_find(start_lr=slice(1e-6, 1e-5), end_lr=slice(1e-2, 1e-1), wd=1e-3)\n",
        "learn.recorder.plot(suggestion=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='99' class='' max='198' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      50.00% [99/198 00:27<00:27 2.4963]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
            "Min numerical gradient: 5.25E-05\n",
            "Min loss divided by 10: 2.29E-04\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "id": "DwFoMF5Rx6Qb",
        "colab_type": "code",
        "outputId": "c041241d-1b9e-4ff2-8aba-9499dfc21f91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(100, max_lr=slice(1e-4, 1e-3), div_factor=10, final_div=30, wd=1e-3, moms=(0.9, 0.8),\n",
        "                    callbacks=[SaveModelCallback(learn, monitor='accuracy', every='improvement', name='best_stage2_bs8_1'),\n",
        "                               EarlyStoppingCallback(learn, monitor='valid_loss', min_delta=0.01, patience=10)])\n",
        "# learn.save('stage-2')\n",
        "learn.recorder.plot_lr(show_moms=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='21' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      21.00% [21/100 25:04<1:34:18]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.106521</td>\n",
              "      <td>0.928920</td>\n",
              "      <td>0.566372</td>\n",
              "      <td>0.808260</td>\n",
              "      <td>0.890855</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.071157</td>\n",
              "      <td>0.867058</td>\n",
              "      <td>0.585546</td>\n",
              "      <td>0.831858</td>\n",
              "      <td>0.910029</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.969542</td>\n",
              "      <td>0.830898</td>\n",
              "      <td>0.606195</td>\n",
              "      <td>0.851032</td>\n",
              "      <td>0.912979</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.950470</td>\n",
              "      <td>0.802050</td>\n",
              "      <td>0.606195</td>\n",
              "      <td>0.859882</td>\n",
              "      <td>0.917404</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.891805</td>\n",
              "      <td>0.763190</td>\n",
              "      <td>0.619469</td>\n",
              "      <td>0.871681</td>\n",
              "      <td>0.917404</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.836853</td>\n",
              "      <td>0.763746</td>\n",
              "      <td>0.609145</td>\n",
              "      <td>0.865782</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.768946</td>\n",
              "      <td>0.745682</td>\n",
              "      <td>0.629794</td>\n",
              "      <td>0.861357</td>\n",
              "      <td>0.918879</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.636834</td>\n",
              "      <td>0.775066</td>\n",
              "      <td>0.626844</td>\n",
              "      <td>0.859882</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.717831</td>\n",
              "      <td>0.729780</td>\n",
              "      <td>0.646018</td>\n",
              "      <td>0.876106</td>\n",
              "      <td>0.926254</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.672106</td>\n",
              "      <td>0.768333</td>\n",
              "      <td>0.607670</td>\n",
              "      <td>0.871681</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.633666</td>\n",
              "      <td>0.698602</td>\n",
              "      <td>0.654867</td>\n",
              "      <td>0.882006</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.620441</td>\n",
              "      <td>0.745225</td>\n",
              "      <td>0.623894</td>\n",
              "      <td>0.877581</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.593969</td>\n",
              "      <td>0.701304</td>\n",
              "      <td>0.648968</td>\n",
              "      <td>0.882006</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.510824</td>\n",
              "      <td>0.756402</td>\n",
              "      <td>0.643068</td>\n",
              "      <td>0.868732</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.453500</td>\n",
              "      <td>0.769424</td>\n",
              "      <td>0.647493</td>\n",
              "      <td>0.870206</td>\n",
              "      <td>0.943953</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.455117</td>\n",
              "      <td>0.860399</td>\n",
              "      <td>0.615044</td>\n",
              "      <td>0.856932</td>\n",
              "      <td>0.927729</td>\n",
              "      <td>01:14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.396948</td>\n",
              "      <td>0.879518</td>\n",
              "      <td>0.616519</td>\n",
              "      <td>0.859882</td>\n",
              "      <td>0.918879</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.401830</td>\n",
              "      <td>0.926257</td>\n",
              "      <td>0.612094</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.920354</td>\n",
              "      <td>01:14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.366273</td>\n",
              "      <td>0.997817</td>\n",
              "      <td>0.613569</td>\n",
              "      <td>0.865782</td>\n",
              "      <td>0.918879</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.390552</td>\n",
              "      <td>0.961598</td>\n",
              "      <td>0.613569</td>\n",
              "      <td>0.848083</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.357090</td>\n",
              "      <td>0.965221</td>\n",
              "      <td>0.606195</td>\n",
              "      <td>0.867257</td>\n",
              "      <td>0.924779</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='85' class='' max='85' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [85/85 00:16<00:00]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.5663716793060303.\n",
            "Better model found at epoch 1 with accuracy value: 0.5855457186698914.\n",
            "Better model found at epoch 2 with accuracy value: 0.6061946749687195.\n",
            "Better model found at epoch 4 with accuracy value: 0.6194690465927124.\n",
            "Better model found at epoch 6 with accuracy value: 0.6297935247421265.\n",
            "Better model found at epoch 8 with accuracy value: 0.6460176706314087.\n",
            "Better model found at epoch 10 with accuracy value: 0.6548672318458557.\n",
            "Epoch 21: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8IE9VWEFIJ8",
        "colab_type": "code",
        "outputId": "04a26b4f-4f06-4485-ee0f-d545e5a08897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "learn.load('best_stage2_bs8')\n",
        "learn.validate(data_test.train_dl)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.2462254, tensor(0.6007), tensor(0.8251), tensor(0.8975)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9w3ZrT1b6AwD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !cp models/train_model/models/best_stage2.pth drive/My\\ Drive/colab_models/efficientnet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twA-vXfT_GVS",
        "colab_type": "code",
        "outputId": "fa1c4085-1416-4340-a459-f4f0907b5a51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "learn.load('best_stage2_bs8')\n",
        "learn.lr_find(start_lr=slice(1e-7, 1e-6), end_lr=slice(1e-2, 1e-1), wd=1e-3)\n",
        "learn.recorder.plot(suggestion=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='1' class='' max='198' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.51% [1/198 00:01<03:31 0.0001]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
            "Failed to compute the gradients, there might not be enough points.\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "id": "690pJjk-1zYN",
        "colab_type": "code",
        "outputId": "b115e54f-f9b3-443e-8c82-5998f9ba9add",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(cyc_len=30, max_lr=slice(1e-5, 1e-4), pct_start=0, final_div=1000, wd=1e-3, moms=(0.9, 0.8),\n",
        "                    callbacks=[SaveModelCallback(learn, monitor='accuracy', every='improvement', name='best_stage3_bs8'),\n",
        "                               EarlyStoppingCallback(learn, monitor='valid_loss', min_delta=0.01, patience=10)]) # warm restart: pct_start=0\n",
        "learn.recorder.plot_losses()\n",
        "learn.recorder.plot_lr(show_moms=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
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              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
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              "      56.67% [17/30 20:28<15:39]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.174079</td>\n",
              "      <td>0.946494</td>\n",
              "      <td>0.657817</td>\n",
              "      <td>0.867257</td>\n",
              "      <td>0.924779</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.143492</td>\n",
              "      <td>0.929806</td>\n",
              "      <td>0.656342</td>\n",
              "      <td>0.876106</td>\n",
              "      <td>0.933628</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.088140</td>\n",
              "      <td>0.937529</td>\n",
              "      <td>0.663717</td>\n",
              "      <td>0.874631</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.079601</td>\n",
              "      <td>0.937910</td>\n",
              "      <td>0.684366</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.074845</td>\n",
              "      <td>0.941912</td>\n",
              "      <td>0.676991</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.923304</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.101393</td>\n",
              "      <td>0.934808</td>\n",
              "      <td>0.678466</td>\n",
              "      <td>0.879056</td>\n",
              "      <td>0.923304</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.097202</td>\n",
              "      <td>0.906022</td>\n",
              "      <td>0.687316</td>\n",
              "      <td>0.877581</td>\n",
              "      <td>0.924779</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.079728</td>\n",
              "      <td>0.913301</td>\n",
              "      <td>0.672566</td>\n",
              "      <td>0.883481</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.046931</td>\n",
              "      <td>0.904003</td>\n",
              "      <td>0.672566</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.936578</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.054699</td>\n",
              "      <td>0.914362</td>\n",
              "      <td>0.676991</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.924779</td>\n",
              "      <td>01:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.064177</td>\n",
              "      <td>0.924572</td>\n",
              "      <td>0.662242</td>\n",
              "      <td>0.877581</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.066537</td>\n",
              "      <td>0.923344</td>\n",
              "      <td>0.665192</td>\n",
              "      <td>0.871681</td>\n",
              "      <td>0.924779</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.065336</td>\n",
              "      <td>0.916478</td>\n",
              "      <td>0.669617</td>\n",
              "      <td>0.868732</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.061774</td>\n",
              "      <td>0.922232</td>\n",
              "      <td>0.681416</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.935103</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.047704</td>\n",
              "      <td>0.918921</td>\n",
              "      <td>0.678466</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.040836</td>\n",
              "      <td>0.912434</td>\n",
              "      <td>0.687316</td>\n",
              "      <td>0.873156</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.915325</td>\n",
              "      <td>0.675516</td>\n",
              "      <td>0.870206</td>\n",
              "      <td>0.932153</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='85' class='' max='85' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [85/85 00:14<00:00]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.6578171253204346.\n",
            "Better model found at epoch 2 with accuracy value: 0.6637167930603027.\n",
            "Better model found at epoch 3 with accuracy value: 0.6843658089637756.\n",
            "Better model found at epoch 6 with accuracy value: 0.6873156428337097.\n",
            "Epoch 17: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "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 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSaD9ymeM-Vp",
        "colab_type": "code",
        "outputId": "e7cc5fe7-08d6-4dd5-ccec-d31028a6f150",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "learn.load('best_stage3_bs8')\n",
        "# preds = learn.get_preds(DatasetType.Test)\n",
        "# from sklearn.metrics import accuracy_score\n",
        "# print(accuracy_score(learn.data.test_ds.y.items.tolist(), torch.argmax(preds[0], dim=1).tolist()))\n",
        "learn.validate(learn.data.test_dl)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.007065, tensor(0.6855), tensor(0.8551), tensor(0.8887)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8iIt5hHN3OE",
        "colab_type": "code",
        "outputId": "4fd91eaa-88b1-468c-df59-d43573130bf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(cyc_len=100, max_lr=slice(1e-6, 1e-5), pct_start=0, final_div=1000, wd=1e-3, moms=(0.9, 0.8),\n",
        "                    callbacks=[SaveModelCallback(learn, monitor='accuracy', every='improvement', name='best_stage4_bs8'),\n",
        "                               EarlyStoppingCallback(learn, monitor='valid_loss', min_delta=0.01, patience=10)]) # warm restart: pct_start=0\n",
        "learn.recorder.plot_losses()\n",
        "learn.recorder.plot_lr(show_moms=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='11' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      11.00% [11/100 12:55<1:44:36]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_3_accuracy</th>\n",
              "      <th>top_5_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.056969</td>\n",
              "      <td>0.900939</td>\n",
              "      <td>0.688791</td>\n",
              "      <td>0.876106</td>\n",
              "      <td>0.926254</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.064460</td>\n",
              "      <td>0.899626</td>\n",
              "      <td>0.679941</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.927729</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.062310</td>\n",
              "      <td>0.902360</td>\n",
              "      <td>0.681416</td>\n",
              "      <td>0.879056</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.073794</td>\n",
              "      <td>0.896537</td>\n",
              "      <td>0.684366</td>\n",
              "      <td>0.883481</td>\n",
              "      <td>0.927729</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.072567</td>\n",
              "      <td>0.902256</td>\n",
              "      <td>0.682891</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.069316</td>\n",
              "      <td>0.904342</td>\n",
              "      <td>0.679941</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.927729</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.068865</td>\n",
              "      <td>0.903479</td>\n",
              "      <td>0.678466</td>\n",
              "      <td>0.879056</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.082261</td>\n",
              "      <td>0.895147</td>\n",
              "      <td>0.690265</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.929204</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.087481</td>\n",
              "      <td>0.901914</td>\n",
              "      <td>0.679941</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.091581</td>\n",
              "      <td>0.900320</td>\n",
              "      <td>0.678466</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.088342</td>\n",
              "      <td>0.895811</td>\n",
              "      <td>0.676991</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.930678</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='85' class='' max='85' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [85/85 00:14<00:00]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.6887905597686768.\n",
            "Better model found at epoch 7 with accuracy value: 0.6902654767036438.\n",
            "Epoch 11: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "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 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5I88fssdV4Dj",
        "colab_type": "code",
        "outputId": "f30b291b-7b57-4da4-b550-6cb1e97d571a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.load('best_stage4')\n",
        "learn.validate(learn.data.test_dl)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-a83a3834a5a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'best_stage4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, file, device, strict, with_opt, purge, remove_module)\u001b[0m\n\u001b[1;32m    271\u001b[0m             \u001b[0mmodel_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mremove_module\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mremove_module_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwith_opt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'opt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[0;32m--> 847\u001b[0;31m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[1;32m    848\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for EfficientNetClassification:\n\tMissing key(s) in state_dict: \"efficient._blocks.2._expand_conv.weight\", \"efficient._blocks.2._bn0.weight\", \"efficient._blocks.2._bn0.bias\", \"efficient._blocks.2._bn0.running_mean\", \"efficient._blocks.2._bn0.running_var\". \n\tUnexpected key(s) in state_dict: \"efficient._blocks.32._expand_conv.weight\", \"efficient._blocks.32._bn0.weight\", \"efficient._blocks.32._bn0.bias\", \"efficient._blocks.32._bn0.running_mean\", \"efficient._blocks.32._bn0.running_var\", \"efficient._blocks.32._bn0.num_batches_tracked\", \"efficient._blocks.32._depthwise_conv.weight\", \"efficient._blocks.32._bn1.weight\", \"efficient._blocks.32._bn1.bias\", \"efficient._blocks.32._bn1.running_mean\", \"efficient._blocks.32._bn1.running_var\", \"efficient._blocks.32._bn1.num_batches_tracked\", \"efficient._blocks.32._se_reduce.weight\", \"efficient._blocks.32._se_reduce.bias\", \"efficient._blocks.32._se_expand.weight\", \"efficient._blocks.32._se_expand.bias\", \"efficient._blocks.32._project_conv.weight\", \"efficient._blocks.32._bn2.weight\", \"efficient._blocks.32._bn2.bias\", \"efficient._blocks.32._bn2.running_mean\", \"efficient._blocks.32._bn2.running_var\", \"efficient._blocks.32._bn2.num_batches_tracked\", \"efficient._blocks.33._expand_conv.weight\", \"efficient._blocks.33._bn0.weight\", \"efficient._blocks.33._bn0.bias\", \"efficient._blocks.33._bn0.running_mean\", \"efficient._blocks.33._bn0.running_var\", \"efficient._blocks.33._bn0.num_batches_tracked\", \"efficient._blocks.33._depthwise_conv.weight\", \"efficient._blocks.33._bn1.weight\", \"efficient._blocks.33._bn1.bias\", \"efficient._blocks.33._bn1.running_mean\", \"efficient._blocks.33._bn1.running_var\", \"efficient._blocks.33._bn1.num_batches_tracked\", \"efficient._blocks.33._se_reduce.weight\", \"efficient._blocks.3...\n\tsize mismatch for efficient._blocks.2._depthwise_conv.weight: copying a param with shape torch.Size([24, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([144, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.2._bn1.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([144]).\n\tsize mismatch for efficient._blocks.2._bn1.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([144]).\n\tsize mismatch for efficient._blocks.2._bn1.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([144]).\n\tsize mismatch for efficient._blocks.2._bn1.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([144]).\n\tsize mismatch for efficient._blocks.2._se_reduce.weight: copying a param with shape torch.Size([6, 24, 1, 1]) from checkpoint, the shape in current model is torch.Size([6, 144, 1, 1]).\n\tsize mismatch for efficient._blocks.2._se_expand.weight: copying a param with shape torch.Size([24, 6, 1, 1]) from checkpoint, the shape in current model is torch.Size([144, 6, 1, 1]).\n\tsize mismatch for efficient._blocks.2._se_expand.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([144]).\n\tsize mismatch for efficient._blocks.2._project_conv.weight: copying a param with shape torch.Size([24, 24, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 144, 1, 1]).\n\tsize mismatch for efficient._blocks.2._bn2.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.2._bn2.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.2._bn2.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.2._bn2.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.3._expand_conv.weight: copying a param with shape torch.Size([144, 24, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 32, 1, 1]).\n\tsize mismatch for efficient._blocks.3._bn0.weight: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn0.bias: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn0.running_mean: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn0.running_var: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._depthwise_conv.weight: copying a param with shape torch.Size([144, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([192, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.3._bn1.weight: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn1.bias: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn1.running_mean: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._bn1.running_var: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._se_reduce.weight: copying a param with shape torch.Size([6, 144, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.3._se_reduce.bias: copying a param with shape torch.Size([6]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for efficient._blocks.3._se_expand.weight: copying a param with shape torch.Size([144, 6, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 8, 1, 1]).\n\tsize mismatch for efficient._blocks.3._se_expand.bias: copying a param with shape torch.Size([144]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.3._project_conv.weight: copying a param with shape torch.Size([40, 144, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.3._bn2.weight: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.3._bn2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.3._bn2.running_mean: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.3._bn2.running_var: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.4._expand_conv.weight: copying a param with shape torch.Size([240, 40, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 32, 1, 1]).\n\tsize mismatch for efficient._blocks.4._bn0.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn0.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn0.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn0.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._depthwise_conv.weight: copying a param with shape torch.Size([240, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([192, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.4._bn1.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn1.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn1.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._bn1.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._se_reduce.weight: copying a param with shape torch.Size([10, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.4._se_reduce.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for efficient._blocks.4._se_expand.weight: copying a param with shape torch.Size([240, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 8, 1, 1]).\n\tsize mismatch for efficient._blocks.4._se_expand.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.4._project_conv.weight: copying a param with shape torch.Size([40, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.4._bn2.weight: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.4._bn2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.4._bn2.running_mean: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.4._bn2.running_var: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.5._expand_conv.weight: copying a param with shape torch.Size([240, 40, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 32, 1, 1]).\n\tsize mismatch for efficient._blocks.5._bn0.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn0.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn0.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn0.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._depthwise_conv.weight: copying a param with shape torch.Size([240, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([192, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.5._bn1.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn1.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn1.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._bn1.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._se_reduce.weight: copying a param with shape torch.Size([10, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.5._se_reduce.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for efficient._blocks.5._se_expand.weight: copying a param with shape torch.Size([240, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 8, 1, 1]).\n\tsize mismatch for efficient._blocks.5._se_expand.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.5._project_conv.weight: copying a param with shape torch.Size([40, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.5._bn2.weight: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.5._bn2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.5._bn2.running_mean: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.5._bn2.running_var: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for efficient._blocks.6._expand_conv.weight: copying a param with shape torch.Size([240, 40, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 32, 1, 1]).\n\tsize mismatch for efficient._blocks.6._bn0.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn0.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn0.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn0.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._depthwise_conv.weight: copying a param with shape torch.Size([240, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([192, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.6._bn1.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn1.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn1.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._bn1.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._se_reduce.weight: copying a param with shape torch.Size([10, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.6._se_reduce.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for efficient._blocks.6._se_expand.weight: copying a param with shape torch.Size([240, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 8, 1, 1]).\n\tsize mismatch for efficient._blocks.6._se_expand.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([192]).\n\tsize mismatch for efficient._blocks.6._project_conv.weight: copying a param with shape torch.Size([40, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([56, 192, 1, 1]).\n\tsize mismatch for efficient._blocks.6._bn2.weight: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.6._bn2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.6._bn2.running_mean: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.6._bn2.running_var: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.7._expand_conv.weight: copying a param with shape torch.Size([240, 40, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 56, 1, 1]).\n\tsize mismatch for efficient._blocks.7._bn0.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn0.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn0.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn0.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._depthwise_conv.weight: copying a param with shape torch.Size([240, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([336, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.7._bn1.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn1.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn1.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._bn1.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._se_reduce.weight: copying a param with shape torch.Size([10, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([14, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.7._se_reduce.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([14]).\n\tsize mismatch for efficient._blocks.7._se_expand.weight: copying a param with shape torch.Size([240, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 14, 1, 1]).\n\tsize mismatch for efficient._blocks.7._se_expand.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.7._project_conv.weight: copying a param with shape torch.Size([40, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([56, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.7._bn2.weight: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.7._bn2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.7._bn2.running_mean: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.7._bn2.running_var: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.8._expand_conv.weight: copying a param with shape torch.Size([240, 40, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 56, 1, 1]).\n\tsize mismatch for efficient._blocks.8._bn0.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn0.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn0.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn0.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._depthwise_conv.weight: copying a param with shape torch.Size([240, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([336, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.8._bn1.weight: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn1.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn1.running_mean: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._bn1.running_var: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._se_reduce.weight: copying a param with shape torch.Size([10, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([14, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.8._se_reduce.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([14]).\n\tsize mismatch for efficient._blocks.8._se_expand.weight: copying a param with shape torch.Size([240, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 14, 1, 1]).\n\tsize mismatch for efficient._blocks.8._se_expand.bias: copying a param with shape torch.Size([240]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.8._project_conv.weight: copying a param with shape torch.Size([64, 240, 1, 1]) from checkpoint, the shape in current model is torch.Size([56, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.8._bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.8._bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.8._bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.8._bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.9._expand_conv.weight: copying a param with shape torch.Size([384, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 56, 1, 1]).\n\tsize mismatch for efficient._blocks.9._bn0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn0.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn0.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._depthwise_conv.weight: copying a param with shape torch.Size([384, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([336, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.9._bn1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._bn1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._se_reduce.weight: copying a param with shape torch.Size([16, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([14, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.9._se_reduce.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([14]).\n\tsize mismatch for efficient._blocks.9._se_expand.weight: copying a param with shape torch.Size([384, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 14, 1, 1]).\n\tsize mismatch for efficient._blocks.9._se_expand.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.9._project_conv.weight: copying a param with shape torch.Size([64, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([56, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.9._bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.9._bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.9._bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.9._bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([56]).\n\tsize mismatch for efficient._blocks.10._expand_conv.weight: copying a param with shape torch.Size([384, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 56, 1, 1]).\n\tsize mismatch for efficient._blocks.10._bn0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn0.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn0.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._depthwise_conv.weight: copying a param with shape torch.Size([384, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([336, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.10._bn1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._bn1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._se_reduce.weight: copying a param with shape torch.Size([16, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([14, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.10._se_reduce.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([14]).\n\tsize mismatch for efficient._blocks.10._se_expand.weight: copying a param with shape torch.Size([384, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([336, 14, 1, 1]).\n\tsize mismatch for efficient._blocks.10._se_expand.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([336]).\n\tsize mismatch for efficient._blocks.10._project_conv.weight: copying a param with shape torch.Size([64, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 336, 1, 1]).\n\tsize mismatch for efficient._blocks.10._bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.10._bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.10._bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.10._bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.11._expand_conv.weight: copying a param with shape torch.Size([384, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.11._bn0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn0.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn0.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._depthwise_conv.weight: copying a param with shape torch.Size([384, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([672, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.11._bn1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._bn1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._se_reduce.weight: copying a param with shape torch.Size([16, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.11._se_reduce.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.11._se_expand.weight: copying a param with shape torch.Size([384, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.11._se_expand.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.11._project_conv.weight: copying a param with shape torch.Size([64, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.11._bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.11._bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.11._bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.11._bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.12._expand_conv.weight: copying a param with shape torch.Size([384, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.12._bn0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn0.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn0.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._depthwise_conv.weight: copying a param with shape torch.Size([384, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([672, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.12._bn1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._bn1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._se_reduce.weight: copying a param with shape torch.Size([16, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.12._se_reduce.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.12._se_expand.weight: copying a param with shape torch.Size([384, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.12._se_expand.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.12._project_conv.weight: copying a param with shape torch.Size([64, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.12._bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.12._bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.12._bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.12._bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.13._expand_conv.weight: copying a param with shape torch.Size([384, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.13._bn0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn0.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn0.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._depthwise_conv.weight: copying a param with shape torch.Size([384, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([672, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.13._bn1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._bn1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._se_reduce.weight: copying a param with shape torch.Size([16, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.13._se_reduce.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.13._se_expand.weight: copying a param with shape torch.Size([384, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.13._se_expand.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.13._project_conv.weight: copying a param with shape torch.Size([128, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.13._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.13._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.13._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.13._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.14._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.14._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([672, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.14._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.14._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.14._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.14._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.14._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.14._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.14._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.14._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.14._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.15._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.15._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([672, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.15._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.15._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.15._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.15._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.15._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.15._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.15._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.15._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.15._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.16._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.16._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([672, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.16._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([28, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.16._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([28]).\n\tsize mismatch for efficient._blocks.16._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([672, 28, 1, 1]).\n\tsize mismatch for efficient._blocks.16._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([672]).\n\tsize mismatch for efficient._blocks.16._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 672, 1, 1]).\n\tsize mismatch for efficient._blocks.16._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.16._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.16._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.16._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.17._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.17._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.17._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.17._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.17._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.17._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.17._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.17._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.17._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.17._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.17._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.18._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.18._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.18._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.18._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.18._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.18._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.18._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.18._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.18._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.18._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.18._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.19._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.19._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.19._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.19._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.19._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.19._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.19._project_conv.weight: copying a param with shape torch.Size([128, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.19._bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.19._bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.19._bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.19._bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.20._expand_conv.weight: copying a param with shape torch.Size([768, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.20._bn0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn0.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn0.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._depthwise_conv.weight: copying a param with shape torch.Size([768, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.20._bn1.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn1.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn1.running_mean: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._bn1.running_var: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._se_reduce.weight: copying a param with shape torch.Size([32, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.20._se_reduce.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.20._se_expand.weight: copying a param with shape torch.Size([768, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.20._se_expand.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.20._project_conv.weight: copying a param with shape torch.Size([176, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.20._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.20._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.20._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.20._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.21._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.21._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.21._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.21._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.21._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.21._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.21._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([160, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.21._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.21._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.21._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.21._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([160]).\n\tsize mismatch for efficient._blocks.22._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 160, 1, 1]).\n\tsize mismatch for efficient._blocks.22._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([960, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.22._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([40, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.22._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([40]).\n\tsize mismatch for efficient._blocks.22._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([960, 40, 1, 1]).\n\tsize mismatch for efficient._blocks.22._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([960]).\n\tsize mismatch for efficient._blocks.22._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 960, 1, 1]).\n\tsize mismatch for efficient._blocks.22._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.22._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.22._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.22._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.23._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.23._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.23._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.23._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.23._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.23._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.23._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.23._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.23._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.23._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.23._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.24._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.24._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.24._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.24._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.24._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.24._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.24._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.24._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.24._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.24._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.24._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.25._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.25._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.25._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.25._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.25._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.25._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.25._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.25._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.25._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.25._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.25._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.26._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.26._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.26._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.26._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.26._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.26._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.26._project_conv.weight: copying a param with shape torch.Size([176, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.26._bn2.weight: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.26._bn2.bias: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.26._bn2.running_mean: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.26._bn2.running_var: copying a param with shape torch.Size([176]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.27._expand_conv.weight: copying a param with shape torch.Size([1056, 176, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.27._bn0.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn0.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn0.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn0.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._depthwise_conv.weight: copying a param with shape torch.Size([1056, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.27._bn1.weight: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn1.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn1.running_mean: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._bn1.running_var: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._se_reduce.weight: copying a param with shape torch.Size([44, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.27._se_reduce.bias: copying a param with shape torch.Size([44]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.27._se_expand.weight: copying a param with shape torch.Size([1056, 44, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.27._se_expand.bias: copying a param with shape torch.Size([1056]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.27._project_conv.weight: copying a param with shape torch.Size([304, 1056, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.27._bn2.weight: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.27._bn2.bias: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.27._bn2.running_mean: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.27._bn2.running_var: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.28._expand_conv.weight: copying a param with shape torch.Size([1824, 304, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.28._bn0.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn0.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn0.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn0.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._depthwise_conv.weight: copying a param with shape torch.Size([1824, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.28._bn1.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn1.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn1.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._bn1.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._se_reduce.weight: copying a param with shape torch.Size([76, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.28._se_reduce.bias: copying a param with shape torch.Size([76]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.28._se_expand.weight: copying a param with shape torch.Size([1824, 76, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.28._se_expand.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.28._project_conv.weight: copying a param with shape torch.Size([304, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.28._bn2.weight: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.28._bn2.bias: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.28._bn2.running_mean: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.28._bn2.running_var: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.29._expand_conv.weight: copying a param with shape torch.Size([1824, 304, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.29._bn0.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn0.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn0.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn0.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._depthwise_conv.weight: copying a param with shape torch.Size([1824, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 5, 5]).\n\tsize mismatch for efficient._blocks.29._bn1.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn1.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn1.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._bn1.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._se_reduce.weight: copying a param with shape torch.Size([76, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.29._se_reduce.bias: copying a param with shape torch.Size([76]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.29._se_expand.weight: copying a param with shape torch.Size([1824, 76, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.29._se_expand.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.29._project_conv.weight: copying a param with shape torch.Size([304, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([272, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.29._bn2.weight: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.29._bn2.bias: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.29._bn2.running_mean: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.29._bn2.running_var: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([272]).\n\tsize mismatch for efficient._blocks.30._expand_conv.weight: copying a param with shape torch.Size([1824, 304, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 272, 1, 1]).\n\tsize mismatch for efficient._blocks.30._bn0.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn0.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn0.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn0.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._depthwise_conv.weight: copying a param with shape torch.Size([1824, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([1632, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.30._bn1.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn1.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn1.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._bn1.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._se_reduce.weight: copying a param with shape torch.Size([76, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([68, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.30._se_reduce.bias: copying a param with shape torch.Size([76]) from checkpoint, the shape in current model is torch.Size([68]).\n\tsize mismatch for efficient._blocks.30._se_expand.weight: copying a param with shape torch.Size([1824, 76, 1, 1]) from checkpoint, the shape in current model is torch.Size([1632, 68, 1, 1]).\n\tsize mismatch for efficient._blocks.30._se_expand.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([1632]).\n\tsize mismatch for efficient._blocks.30._project_conv.weight: copying a param with shape torch.Size([304, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([448, 1632, 1, 1]).\n\tsize mismatch for efficient._blocks.30._bn2.weight: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.30._bn2.bias: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.30._bn2.running_mean: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.30._bn2.running_var: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.31._expand_conv.weight: copying a param with shape torch.Size([1824, 304, 1, 1]) from checkpoint, the shape in current model is torch.Size([2688, 448, 1, 1]).\n\tsize mismatch for efficient._blocks.31._bn0.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn0.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn0.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn0.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._depthwise_conv.weight: copying a param with shape torch.Size([1824, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([2688, 1, 3, 3]).\n\tsize mismatch for efficient._blocks.31._bn1.weight: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn1.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn1.running_mean: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._bn1.running_var: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._se_reduce.weight: copying a param with shape torch.Size([76, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([112, 2688, 1, 1]).\n\tsize mismatch for efficient._blocks.31._se_reduce.bias: copying a param with shape torch.Size([76]) from checkpoint, the shape in current model is torch.Size([112]).\n\tsize mismatch for efficient._blocks.31._se_expand.weight: copying a param with shape torch.Size([1824, 76, 1, 1]) from checkpoint, the shape in current model is torch.Size([2688, 112, 1, 1]).\n\tsize mismatch for efficient._blocks.31._se_expand.bias: copying a param with shape torch.Size([1824]) from checkpoint, the shape in current model is torch.Size([2688]).\n\tsize mismatch for efficient._blocks.31._project_conv.weight: copying a param with shape torch.Size([304, 1824, 1, 1]) from checkpoint, the shape in current model is torch.Size([448, 2688, 1, 1]).\n\tsize mismatch for efficient._blocks.31._bn2.weight: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.31._bn2.bias: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.31._bn2.running_mean: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._blocks.31._bn2.running_var: copying a param with shape torch.Size([304]) from checkpoint, the shape in current model is torch.Size([448]).\n\tsize mismatch for efficient._conv_head.weight: copying a param with shape torch.Size([2048, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([1792, 448, 1, 1]).\n\tsize mismatch for efficient._bn1.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1792]).\n\tsize mismatch for efficient._bn1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1792]).\n\tsize mismatch for efficient._bn1.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1792]).\n\tsize mismatch for efficient._bn1.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1792]).\n\tsize mismatch for efficient._fc.weight: copying a param with shape torch.Size([20, 2048]) from checkpoint, the shape in current model is torch.Size([20, 1792]).\n\tsize mismatch for _mlp.1.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([512, 1792])."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY_0X0lj8vk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp models/train_model/models/best_stage3_kappa.pth drive/My\\ Drive/colab_models/efficientnet\n",
        "!cp models/train_model/models/best_stage3_accuracy.pth drive/My\\ Drive/colab_models/efficientnet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhYavq07Ry_N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}