package_test.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7Biz40S1C_N"
      },
      "source": [
        "# *labrary usage and prametrs*\n",
        "\n",
        "importing needed requered labaries for work\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fAvN1eFk6d_",
        "outputId": "859217e1-4e57-439f-ba91-e2bee2db3aee"
      },
      "source": [
        "!pip install timm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting timm\n",
            "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
            "\u001b[?25l\r\u001b[K     |▉                               | 10 kB 42.8 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 20 kB 9.0 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 30 kB 8.0 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 40 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |████▍                           | 51 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 61 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |██████                          | 71 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |███████                         | 81 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 92 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 102 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 112 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 122 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 133 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 143 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 153 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 163 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 174 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 184 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 194 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 204 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 215 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 225 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 235 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 245 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 256 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 266 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 276 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 286 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▏      | 296 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 307 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 317 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 327 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 337 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 348 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 358 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 368 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 376 kB 4.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from timm) (1.9.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm) (0.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->timm) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (1.19.5)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.4.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVgPSsC8lHuT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9BuDpDAlY4u",
        "outputId": "4769f4d7-bc38-423c-9ece-effc15add005"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8ov-sE0k6eF"
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__  import division\n",
        "from __future__ import print_function\n",
        "import numpy as np\n",
        "from skimage.io import imread\n",
        "from skimage.transform import resize\n",
        "import cv2\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.autograd import Variable\n",
        "from torchvision import datasets, models, transforms  \n",
        "from torch.utils.data.sampler import SubsetRandomSampler  \n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn import model_selection, metrics\n",
        "\n",
        "import pandas as pd\n",
        "import torch.nn.functional as F\n",
        "import os"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKSmXTBR1ZEm"
      },
      "source": [
        "# import the library and install it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwdVQBWHYz71",
        "outputId": "0a3c08c7-d653-4826-e068-7419357c2176"
      },
      "source": [
        "!pip install menovideo"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting menovideo\n",
            "  Downloading menovideo-0.5.0-py3-none-any.whl (6.4 kB)\n",
            "Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from menovideo) (1.9.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from menovideo) (0.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->menovideo) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->menovideo) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->menovideo) (1.19.5)\n",
            "Installing collected packages: menovideo\n",
            "Successfully installed menovideo-0.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMEYMKO1k6eH"
      },
      "source": [
        "import menovideo.menovideo as menoformer\n",
        "import menovideo.videopre as vide_reader"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_PDqfHW3fAe"
      },
      "source": [
        "**example of using defualt DeVTR without pre-trained wights**\n",
        " \n",
        "the parameters is :\n",
        "\n",
        "1.   w : is the  path for pre-traied DeVtr model\n",
        "2.  base : is the base Conv net that work as input embding layer if set to 1.  3.  default will use paper default work with vgg19\n",
        "4.  classifier :  is the classifier DNN that recive  the output from the 1.  5.  transformer encoder if use default will use same architcture of our main paper\n",
        "6.  if you want to use your own architcture just pass to it any  nn.sequential  network will work\n",
        "7.  mid_layer : is the FNN that diractly after the transformer  output\n",
        "8.  mid_drop : is the dropout for the mid_layer\n",
        "9.  num_class : is the number of the output class\n",
        "10.   dim_embd : is the dimionsionalty that we want our input to be  after 1.  11.  transformed by time distrpyted convnet\n",
        "12.   dr_rate :   is the droupout of the transformer encoder\n",
        "13.  time_stp : is the number of frames of the input \n",
        "\n",
        " \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTiWiJQuk6eH",
        "outputId": "7e7286db-052d-4755-ddb8-344768b9f481"
      },
      "source": [
        "#not pre-trained\n",
        "'''\n",
        "w : is the  path for pre-traied DeVtr model\n",
        "base : is the base Conv net that work as input embding layer if set to default will use paper default work with vgg19\n",
        "classifier :  is the classifier DNN that recive  the output from the transformer encoder if use default will use same architcture of our main paper\n",
        "if you want to use your own architcture just pass to it any  nn.sequential network will work\n",
        "mid_layer : is the FNN that diractly after the transformer  output\n",
        "mid_drop : is the dropout for the mid_layer\n",
        "num_class : is the number of the output class\n",
        " dim_embd : is the dimionsionalty that we want our input to be  after transformed by time distrpyted convnet\n",
        " dr_rate :   is the droupout of the transformer encoder\n",
        "time_stp : is the number of frames of the input \n",
        "\n",
        "defualt values \n",
        "menoformer.DeVTr(w= 'none' , base ='default' ,classifier='default',mid_layer=1024,mid_drop=0.4, num_classes=1 , dim_embd = 512  ,dr_rate= 0.1  , time_stp = 40)\n",
        "'''\n",
        "model = menoformer.DeVTr()\n",
        "model"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg19_bn-c79401a0.pth\" to /root/.cache/torch/hub/checkpoints/vgg19_bn-c79401a0.pth\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): VGG(\n",
              "        (features): Sequential(\n",
              "          (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (9): ReLU(inplace=True)\n",
              "          (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (12): ReLU(inplace=True)\n",
              "          (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (16): ReLU(inplace=True)\n",
              "          (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (19): ReLU(inplace=True)\n",
              "          (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (22): ReLU(inplace=True)\n",
              "          (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (25): ReLU(inplace=True)\n",
              "          (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (29): ReLU(inplace=True)\n",
              "          (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (32): ReLU(inplace=True)\n",
              "          (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (35): ReLU(inplace=True)\n",
              "          (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (38): ReLU(inplace=True)\n",
              "          (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (42): ReLU(inplace=True)\n",
              "          (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (45): ReLU(inplace=True)\n",
              "          (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (48): ReLU(inplace=True)\n",
              "          (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (51): ReLU(inplace=True)\n",
              "          (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (pre_logits): ConvMlp(\n",
              "          (fc1): Conv2d(512, 4096, kernel_size=(7, 7), stride=(1, 1))\n",
              "          (act1): ReLU(inplace=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "          (fc2): Conv2d(4096, 4096, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act2): ReLU(inplace=True)\n",
              "        )\n",
              "        (head): ClassifierHead(\n",
              "          (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "          (fc): Linear(in_features=4096, out_features=512, bias=True)\n",
              "          (flatten): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "      (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=20480, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Linear(in_features=1024, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4Ewr3lx5Fp8"
      },
      "source": [
        "**example of loading pre-trained DeVTR**\n",
        "\n",
        "#download the wights from\n",
        "# https://drive.google.com/file/d/1s7Z1c-4zC522BFVM5EiZDMQLe6ZV8QQh/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPO9mywq5gyU",
        "outputId": "b912a7af-3081-4e8e-9fa8-0403c37e9a81"
      },
      "source": [
        "#pre-trained \n",
        "#wight = '/content/drive/MyDrive/Colab Notebooks/vg19bn-40-convtransformer-ep-0.pth'\n",
        "wight = 'drive/MyDrive/Colab Notebooks/transformers/violance-detaction-myresearch/vg19bn40convtransformer-ep-0.pth'\n",
        "model2 = menoformer.DeVTr(w= wight , base ='default')\n",
        "model2"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): VGG(\n",
              "        (features): Sequential(\n",
              "          (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (9): ReLU(inplace=True)\n",
              "          (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (12): ReLU(inplace=True)\n",
              "          (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (16): ReLU(inplace=True)\n",
              "          (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (19): ReLU(inplace=True)\n",
              "          (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (22): ReLU(inplace=True)\n",
              "          (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (25): ReLU(inplace=True)\n",
              "          (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (29): ReLU(inplace=True)\n",
              "          (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (32): ReLU(inplace=True)\n",
              "          (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (35): ReLU(inplace=True)\n",
              "          (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (38): ReLU(inplace=True)\n",
              "          (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (42): ReLU(inplace=True)\n",
              "          (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (45): ReLU(inplace=True)\n",
              "          (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (48): ReLU(inplace=True)\n",
              "          (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (51): ReLU(inplace=True)\n",
              "          (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (pre_logits): ConvMlp(\n",
              "          (fc1): Conv2d(512, 4096, kernel_size=(7, 7), stride=(1, 1))\n",
              "          (act1): ReLU(inplace=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "          (fc2): Conv2d(4096, 4096, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act2): ReLU(inplace=True)\n",
              "        )\n",
              "        (head): ClassifierHead(\n",
              "          (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "          (fc): Linear(in_features=4096, out_features=512, bias=True)\n",
              "          (flatten): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "      (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=20480, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Linear(in_features=1024, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3StpWRfE68NU"
      },
      "source": [
        "# **use costume Conv net as input embding layer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYzRZ2ask6eI",
        "outputId": "85c356e1-b151-40e1-e1b9-e452d7dd2c55"
      },
      "source": [
        "import timm\n",
        "from pprint import pprint\n",
        "model_names = timm.list_models(pretrained=True)\n",
        "pprint(model_names)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['adv_inception_v3',\n",
            " 'cait_m36_384',\n",
            " 'cait_m48_448',\n",
            " 'cait_s24_224',\n",
            " 'cait_s24_384',\n",
            " 'cait_s36_384',\n",
            " 'cait_xs24_384',\n",
            " 'cait_xxs24_224',\n",
            " 'cait_xxs24_384',\n",
            " 'cait_xxs36_224',\n",
            " 'cait_xxs36_384',\n",
            " 'coat_lite_mini',\n",
            " 'coat_lite_small',\n",
            " 'coat_lite_tiny',\n",
            " 'coat_mini',\n",
            " 'coat_tiny',\n",
            " 'convit_base',\n",
            " 'convit_small',\n",
            " 'convit_tiny',\n",
            " 'cspdarknet53',\n",
            " 'cspresnet50',\n",
            " 'cspresnext50',\n",
            " 'deit_base_distilled_patch16_224',\n",
            " 'deit_base_distilled_patch16_384',\n",
            " 'deit_base_patch16_224',\n",
            " 'deit_base_patch16_384',\n",
            " 'deit_small_distilled_patch16_224',\n",
            " 'deit_small_patch16_224',\n",
            " 'deit_tiny_distilled_patch16_224',\n",
            " 'deit_tiny_patch16_224',\n",
            " 'densenet121',\n",
            " 'densenet161',\n",
            " 'densenet169',\n",
            " 'densenet201',\n",
            " 'densenetblur121d',\n",
            " 'dla34',\n",
            " 'dla46_c',\n",
            " 'dla46x_c',\n",
            " 'dla60',\n",
            " 'dla60_res2net',\n",
            " 'dla60_res2next',\n",
            " 'dla60x',\n",
            " 'dla60x_c',\n",
            " 'dla102',\n",
            " 'dla102x',\n",
            " 'dla102x2',\n",
            " 'dla169',\n",
            " 'dm_nfnet_f0',\n",
            " 'dm_nfnet_f1',\n",
            " 'dm_nfnet_f2',\n",
            " 'dm_nfnet_f3',\n",
            " 'dm_nfnet_f4',\n",
            " 'dm_nfnet_f5',\n",
            " 'dm_nfnet_f6',\n",
            " 'dpn68',\n",
            " 'dpn68b',\n",
            " 'dpn92',\n",
            " 'dpn98',\n",
            " 'dpn107',\n",
            " 'dpn131',\n",
            " 'eca_nfnet_l0',\n",
            " 'eca_nfnet_l1',\n",
            " 'eca_nfnet_l2',\n",
            " 'ecaresnet26t',\n",
            " 'ecaresnet50d',\n",
            " 'ecaresnet50d_pruned',\n",
            " 'ecaresnet50t',\n",
            " 'ecaresnet101d',\n",
            " 'ecaresnet101d_pruned',\n",
            " 'ecaresnet269d',\n",
            " 'ecaresnetlight',\n",
            " 'efficientnet_b0',\n",
            " 'efficientnet_b1',\n",
            " 'efficientnet_b1_pruned',\n",
            " 'efficientnet_b2',\n",
            " 'efficientnet_b2_pruned',\n",
            " 'efficientnet_b3',\n",
            " 'efficientnet_b3_pruned',\n",
            " 'efficientnet_b4',\n",
            " 'efficientnet_el',\n",
            " 'efficientnet_el_pruned',\n",
            " 'efficientnet_em',\n",
            " 'efficientnet_es',\n",
            " 'efficientnet_es_pruned',\n",
            " 'efficientnet_lite0',\n",
            " 'efficientnetv2_rw_m',\n",
            " 'efficientnetv2_rw_s',\n",
            " 'ens_adv_inception_resnet_v2',\n",
            " 'ese_vovnet19b_dw',\n",
            " 'ese_vovnet39b',\n",
            " 'fbnetc_100',\n",
            " 'gernet_l',\n",
            " 'gernet_m',\n",
            " 'gernet_s',\n",
            " 'ghostnet_100',\n",
            " 'gluon_inception_v3',\n",
            " 'gluon_resnet18_v1b',\n",
            " 'gluon_resnet34_v1b',\n",
            " 'gluon_resnet50_v1b',\n",
            " 'gluon_resnet50_v1c',\n",
            " 'gluon_resnet50_v1d',\n",
            " 'gluon_resnet50_v1s',\n",
            " 'gluon_resnet101_v1b',\n",
            " 'gluon_resnet101_v1c',\n",
            " 'gluon_resnet101_v1d',\n",
            " 'gluon_resnet101_v1s',\n",
            " 'gluon_resnet152_v1b',\n",
            " 'gluon_resnet152_v1c',\n",
            " 'gluon_resnet152_v1d',\n",
            " 'gluon_resnet152_v1s',\n",
            " 'gluon_resnext50_32x4d',\n",
            " 'gluon_resnext101_32x4d',\n",
            " 'gluon_resnext101_64x4d',\n",
            " 'gluon_senet154',\n",
            " 'gluon_seresnext50_32x4d',\n",
            " 'gluon_seresnext101_32x4d',\n",
            " 'gluon_seresnext101_64x4d',\n",
            " 'gluon_xception65',\n",
            " 'gmixer_24_224',\n",
            " 'gmlp_s16_224',\n",
            " 'hardcorenas_a',\n",
            " 'hardcorenas_b',\n",
            " 'hardcorenas_c',\n",
            " 'hardcorenas_d',\n",
            " 'hardcorenas_e',\n",
            " 'hardcorenas_f',\n",
            " 'hrnet_w18',\n",
            " 'hrnet_w18_small',\n",
            " 'hrnet_w18_small_v2',\n",
            " 'hrnet_w30',\n",
            " 'hrnet_w32',\n",
            " 'hrnet_w40',\n",
            " 'hrnet_w44',\n",
            " 'hrnet_w48',\n",
            " 'hrnet_w64',\n",
            " 'ig_resnext101_32x8d',\n",
            " 'ig_resnext101_32x16d',\n",
            " 'ig_resnext101_32x32d',\n",
            " 'ig_resnext101_32x48d',\n",
            " 'inception_resnet_v2',\n",
            " 'inception_v3',\n",
            " 'inception_v4',\n",
            " 'legacy_senet154',\n",
            " 'legacy_seresnet18',\n",
            " 'legacy_seresnet34',\n",
            " 'legacy_seresnet50',\n",
            " 'legacy_seresnet101',\n",
            " 'legacy_seresnet152',\n",
            " 'legacy_seresnext26_32x4d',\n",
            " 'legacy_seresnext50_32x4d',\n",
            " 'legacy_seresnext101_32x4d',\n",
            " 'levit_128',\n",
            " 'levit_128s',\n",
            " 'levit_192',\n",
            " 'levit_256',\n",
            " 'levit_384',\n",
            " 'mixer_b16_224',\n",
            " 'mixer_b16_224_in21k',\n",
            " 'mixer_b16_224_miil',\n",
            " 'mixer_b16_224_miil_in21k',\n",
            " 'mixer_l16_224',\n",
            " 'mixer_l16_224_in21k',\n",
            " 'mixnet_l',\n",
            " 'mixnet_m',\n",
            " 'mixnet_s',\n",
            " 'mixnet_xl',\n",
            " 'mnasnet_100',\n",
            " 'mobilenetv2_100',\n",
            " 'mobilenetv2_110d',\n",
            " 'mobilenetv2_120d',\n",
            " 'mobilenetv2_140',\n",
            " 'mobilenetv3_large_100',\n",
            " 'mobilenetv3_large_100_miil',\n",
            " 'mobilenetv3_large_100_miil_in21k',\n",
            " 'mobilenetv3_rw',\n",
            " 'nasnetalarge',\n",
            " 'nf_regnet_b1',\n",
            " 'nf_resnet50',\n",
            " 'nfnet_l0',\n",
            " 'pit_b_224',\n",
            " 'pit_b_distilled_224',\n",
            " 'pit_s_224',\n",
            " 'pit_s_distilled_224',\n",
            " 'pit_ti_224',\n",
            " 'pit_ti_distilled_224',\n",
            " 'pit_xs_224',\n",
            " 'pit_xs_distilled_224',\n",
            " 'pnasnet5large',\n",
            " 'regnetx_002',\n",
            " 'regnetx_004',\n",
            " 'regnetx_006',\n",
            " 'regnetx_008',\n",
            " 'regnetx_016',\n",
            " 'regnetx_032',\n",
            " 'regnetx_040',\n",
            " 'regnetx_064',\n",
            " 'regnetx_080',\n",
            " 'regnetx_120',\n",
            " 'regnetx_160',\n",
            " 'regnetx_320',\n",
            " 'regnety_002',\n",
            " 'regnety_004',\n",
            " 'regnety_006',\n",
            " 'regnety_008',\n",
            " 'regnety_016',\n",
            " 'regnety_032',\n",
            " 'regnety_040',\n",
            " 'regnety_064',\n",
            " 'regnety_080',\n",
            " 'regnety_120',\n",
            " 'regnety_160',\n",
            " 'regnety_320',\n",
            " 'repvgg_a2',\n",
            " 'repvgg_b0',\n",
            " 'repvgg_b1',\n",
            " 'repvgg_b1g4',\n",
            " 'repvgg_b2',\n",
            " 'repvgg_b2g4',\n",
            " 'repvgg_b3',\n",
            " 'repvgg_b3g4',\n",
            " 'res2net50_14w_8s',\n",
            " 'res2net50_26w_4s',\n",
            " 'res2net50_26w_6s',\n",
            " 'res2net50_26w_8s',\n",
            " 'res2net50_48w_2s',\n",
            " 'res2net101_26w_4s',\n",
            " 'res2next50',\n",
            " 'resmlp_12_224',\n",
            " 'resmlp_12_distilled_224',\n",
            " 'resmlp_24_224',\n",
            " 'resmlp_24_distilled_224',\n",
            " 'resmlp_36_224',\n",
            " 'resmlp_36_distilled_224',\n",
            " 'resmlp_big_24_224',\n",
            " 'resmlp_big_24_224_in22ft1k',\n",
            " 'resmlp_big_24_distilled_224',\n",
            " 'resnest14d',\n",
            " 'resnest26d',\n",
            " 'resnest50d',\n",
            " 'resnest50d_1s4x24d',\n",
            " 'resnest50d_4s2x40d',\n",
            " 'resnest101e',\n",
            " 'resnest200e',\n",
            " 'resnest269e',\n",
            " 'resnet18',\n",
            " 'resnet18d',\n",
            " 'resnet26',\n",
            " 'resnet26d',\n",
            " 'resnet34',\n",
            " 'resnet34d',\n",
            " 'resnet50',\n",
            " 'resnet50d',\n",
            " 'resnet51q',\n",
            " 'resnet101d',\n",
            " 'resnet152d',\n",
            " 'resnet200d',\n",
            " 'resnetblur50',\n",
            " 'resnetrs50',\n",
            " 'resnetrs101',\n",
            " 'resnetrs152',\n",
            " 'resnetrs200',\n",
            " 'resnetrs270',\n",
            " 'resnetrs350',\n",
            " 'resnetrs420',\n",
            " 'resnetv2_50x1_bit_distilled',\n",
            " 'resnetv2_50x1_bitm',\n",
            " 'resnetv2_50x1_bitm_in21k',\n",
            " 'resnetv2_50x3_bitm',\n",
            " 'resnetv2_50x3_bitm_in21k',\n",
            " 'resnetv2_101x1_bitm',\n",
            " 'resnetv2_101x1_bitm_in21k',\n",
            " 'resnetv2_101x3_bitm',\n",
            " 'resnetv2_101x3_bitm_in21k',\n",
            " 'resnetv2_152x2_bit_teacher',\n",
            " 'resnetv2_152x2_bit_teacher_384',\n",
            " 'resnetv2_152x2_bitm',\n",
            " 'resnetv2_152x2_bitm_in21k',\n",
            " 'resnetv2_152x4_bitm',\n",
            " 'resnetv2_152x4_bitm_in21k',\n",
            " 'resnext50_32x4d',\n",
            " 'resnext50d_32x4d',\n",
            " 'resnext101_32x8d',\n",
            " 'rexnet_100',\n",
            " 'rexnet_130',\n",
            " 'rexnet_150',\n",
            " 'rexnet_200',\n",
            " 'selecsls42b',\n",
            " 'selecsls60',\n",
            " 'selecsls60b',\n",
            " 'semnasnet_100',\n",
            " 'seresnet50',\n",
            " 'seresnet152d',\n",
            " 'seresnext26d_32x4d',\n",
            " 'seresnext26t_32x4d',\n",
            " 'seresnext50_32x4d',\n",
            " 'skresnet18',\n",
            " 'skresnet34',\n",
            " 'skresnext50_32x4d',\n",
            " 'spnasnet_100',\n",
            " 'ssl_resnet18',\n",
            " 'ssl_resnet50',\n",
            " 'ssl_resnext50_32x4d',\n",
            " 'ssl_resnext101_32x4d',\n",
            " 'ssl_resnext101_32x8d',\n",
            " 'ssl_resnext101_32x16d',\n",
            " 'swin_base_patch4_window7_224',\n",
            " 'swin_base_patch4_window7_224_in22k',\n",
            " 'swin_base_patch4_window12_384',\n",
            " 'swin_base_patch4_window12_384_in22k',\n",
            " 'swin_large_patch4_window7_224',\n",
            " 'swin_large_patch4_window7_224_in22k',\n",
            " 'swin_large_patch4_window12_384',\n",
            " 'swin_large_patch4_window12_384_in22k',\n",
            " 'swin_small_patch4_window7_224',\n",
            " 'swin_tiny_patch4_window7_224',\n",
            " 'swsl_resnet18',\n",
            " 'swsl_resnet50',\n",
            " 'swsl_resnext50_32x4d',\n",
            " 'swsl_resnext101_32x4d',\n",
            " 'swsl_resnext101_32x8d',\n",
            " 'swsl_resnext101_32x16d',\n",
            " 'tf_efficientnet_b0',\n",
            " 'tf_efficientnet_b0_ap',\n",
            " 'tf_efficientnet_b0_ns',\n",
            " 'tf_efficientnet_b1',\n",
            " 'tf_efficientnet_b1_ap',\n",
            " 'tf_efficientnet_b1_ns',\n",
            " 'tf_efficientnet_b2',\n",
            " 'tf_efficientnet_b2_ap',\n",
            " 'tf_efficientnet_b2_ns',\n",
            " 'tf_efficientnet_b3',\n",
            " 'tf_efficientnet_b3_ap',\n",
            " 'tf_efficientnet_b3_ns',\n",
            " 'tf_efficientnet_b4',\n",
            " 'tf_efficientnet_b4_ap',\n",
            " 'tf_efficientnet_b4_ns',\n",
            " 'tf_efficientnet_b5',\n",
            " 'tf_efficientnet_b5_ap',\n",
            " 'tf_efficientnet_b5_ns',\n",
            " 'tf_efficientnet_b6',\n",
            " 'tf_efficientnet_b6_ap',\n",
            " 'tf_efficientnet_b6_ns',\n",
            " 'tf_efficientnet_b7',\n",
            " 'tf_efficientnet_b7_ap',\n",
            " 'tf_efficientnet_b7_ns',\n",
            " 'tf_efficientnet_b8',\n",
            " 'tf_efficientnet_b8_ap',\n",
            " 'tf_efficientnet_cc_b0_4e',\n",
            " 'tf_efficientnet_cc_b0_8e',\n",
            " 'tf_efficientnet_cc_b1_8e',\n",
            " 'tf_efficientnet_el',\n",
            " 'tf_efficientnet_em',\n",
            " 'tf_efficientnet_es',\n",
            " 'tf_efficientnet_l2_ns',\n",
            " 'tf_efficientnet_l2_ns_475',\n",
            " 'tf_efficientnet_lite0',\n",
            " 'tf_efficientnet_lite1',\n",
            " 'tf_efficientnet_lite2',\n",
            " 'tf_efficientnet_lite3',\n",
            " 'tf_efficientnet_lite4',\n",
            " 'tf_efficientnetv2_b0',\n",
            " 'tf_efficientnetv2_b1',\n",
            " 'tf_efficientnetv2_b2',\n",
            " 'tf_efficientnetv2_b3',\n",
            " 'tf_efficientnetv2_l',\n",
            " 'tf_efficientnetv2_l_in21ft1k',\n",
            " 'tf_efficientnetv2_l_in21k',\n",
            " 'tf_efficientnetv2_m',\n",
            " 'tf_efficientnetv2_m_in21ft1k',\n",
            " 'tf_efficientnetv2_m_in21k',\n",
            " 'tf_efficientnetv2_s',\n",
            " 'tf_efficientnetv2_s_in21ft1k',\n",
            " 'tf_efficientnetv2_s_in21k',\n",
            " 'tf_inception_v3',\n",
            " 'tf_mixnet_l',\n",
            " 'tf_mixnet_m',\n",
            " 'tf_mixnet_s',\n",
            " 'tf_mobilenetv3_large_075',\n",
            " 'tf_mobilenetv3_large_100',\n",
            " 'tf_mobilenetv3_large_minimal_100',\n",
            " 'tf_mobilenetv3_small_075',\n",
            " 'tf_mobilenetv3_small_100',\n",
            " 'tf_mobilenetv3_small_minimal_100',\n",
            " 'tnt_s_patch16_224',\n",
            " 'tresnet_l',\n",
            " 'tresnet_l_448',\n",
            " 'tresnet_m',\n",
            " 'tresnet_m_448',\n",
            " 'tresnet_m_miil_in21k',\n",
            " 'tresnet_xl',\n",
            " 'tresnet_xl_448',\n",
            " 'tv_densenet121',\n",
            " 'tv_resnet34',\n",
            " 'tv_resnet50',\n",
            " 'tv_resnet101',\n",
            " 'tv_resnet152',\n",
            " 'tv_resnext50_32x4d',\n",
            " 'twins_pcpvt_base',\n",
            " 'twins_pcpvt_large',\n",
            " 'twins_pcpvt_small',\n",
            " 'twins_svt_base',\n",
            " 'twins_svt_large',\n",
            " 'twins_svt_small',\n",
            " 'vgg11',\n",
            " 'vgg11_bn',\n",
            " 'vgg13',\n",
            " 'vgg13_bn',\n",
            " 'vgg16',\n",
            " 'vgg16_bn',\n",
            " 'vgg19',\n",
            " 'vgg19_bn',\n",
            " 'visformer_small',\n",
            " 'vit_base_patch16_224',\n",
            " 'vit_base_patch16_224_in21k',\n",
            " 'vit_base_patch16_224_miil',\n",
            " 'vit_base_patch16_224_miil_in21k',\n",
            " 'vit_base_patch16_384',\n",
            " 'vit_base_patch32_224',\n",
            " 'vit_base_patch32_224_in21k',\n",
            " 'vit_base_patch32_384',\n",
            " 'vit_base_r50_s16_224_in21k',\n",
            " 'vit_base_r50_s16_384',\n",
            " 'vit_huge_patch14_224_in21k',\n",
            " 'vit_large_patch16_224',\n",
            " 'vit_large_patch16_224_in21k',\n",
            " 'vit_large_patch16_384',\n",
            " 'vit_large_patch32_224_in21k',\n",
            " 'vit_large_patch32_384',\n",
            " 'vit_large_r50_s32_224',\n",
            " 'vit_large_r50_s32_224_in21k',\n",
            " 'vit_large_r50_s32_384',\n",
            " 'vit_small_patch16_224',\n",
            " 'vit_small_patch16_224_in21k',\n",
            " 'vit_small_patch16_384',\n",
            " 'vit_small_patch32_224',\n",
            " 'vit_small_patch32_224_in21k',\n",
            " 'vit_small_patch32_384',\n",
            " 'vit_small_r26_s32_224',\n",
            " 'vit_small_r26_s32_224_in21k',\n",
            " 'vit_small_r26_s32_384',\n",
            " 'vit_tiny_patch16_224',\n",
            " 'vit_tiny_patch16_224_in21k',\n",
            " 'vit_tiny_patch16_384',\n",
            " 'vit_tiny_r_s16_p8_224',\n",
            " 'vit_tiny_r_s16_p8_224_in21k',\n",
            " 'vit_tiny_r_s16_p8_384',\n",
            " 'wide_resnet50_2',\n",
            " 'wide_resnet101_2',\n",
            " 'xception',\n",
            " 'xception41',\n",
            " 'xception65',\n",
            " 'xception71']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c4NlsOxk6eI",
        "outputId": "ebde38cf-79db-40df-da6c-d5e88e900651"
      },
      "source": [
        "dim_embd = 128\n",
        "baseModel  =  timm.create_model('resnet26', pretrained=True , num_classes=dim_embd )\n",
        "\n",
        "model_res = menoformer.DeVTr(base = baseModel , num_classes=10 , dim_embd=dim_embd)\n",
        "model_res"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth\" to /root/.cache/torch/hub/checkpoints/resnet26-9aa10e23.pth\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): ResNet(\n",
              "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "        (layer1): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer2): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer3): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer4): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "        (fc): Linear(in_features=2048, out_features=128, bias=True)\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "      (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=5120, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Linear(in_features=1024, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHmbNhSi9SQq"
      },
      "source": [
        "# **use costume classifier network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lpodko79Rbw",
        "outputId": "10c7c8a8-e6f9-4b1d-dc11-88b028ceef78"
      },
      "source": [
        "dim_embd = 128\n",
        "time_stp = 40\n",
        "num_classes = 2\n",
        "mid_layer = 1024\n",
        "mid_drop = 0.35\n",
        "classifier  = nn.Sequential(  nn.Linear(time_stp * dim_embd, mid_layer),\n",
        "                                nn.Dropout(mid_drop),\n",
        "                                nn.ReLU(),              \n",
        "            nn.Linear(mid_layer, num_classes)\n",
        "            )\n",
        "model_cls = menoformer.DeVTr( classifier= classifier, num_classes=num_classes , dim_embd=dim_embd , time_stp=time_stp)\n",
        "model_cls"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): VGG(\n",
              "        (features): Sequential(\n",
              "          (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (9): ReLU(inplace=True)\n",
              "          (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (12): ReLU(inplace=True)\n",
              "          (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (16): ReLU(inplace=True)\n",
              "          (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (19): ReLU(inplace=True)\n",
              "          (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (22): ReLU(inplace=True)\n",
              "          (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (25): ReLU(inplace=True)\n",
              "          (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (29): ReLU(inplace=True)\n",
              "          (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (32): ReLU(inplace=True)\n",
              "          (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (35): ReLU(inplace=True)\n",
              "          (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (38): ReLU(inplace=True)\n",
              "          (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (42): ReLU(inplace=True)\n",
              "          (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (45): ReLU(inplace=True)\n",
              "          (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (48): ReLU(inplace=True)\n",
              "          (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (51): ReLU(inplace=True)\n",
              "          (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (pre_logits): ConvMlp(\n",
              "          (fc1): Conv2d(512, 4096, kernel_size=(7, 7), stride=(1, 1))\n",
              "          (act1): ReLU(inplace=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "          (fc2): Conv2d(4096, 4096, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act2): ReLU(inplace=True)\n",
              "        )\n",
              "        (head): ClassifierHead(\n",
              "          (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "          (fc): Linear(in_features=4096, out_features=128, bias=True)\n",
              "          (flatten): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "      (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=5120, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Linear(in_features=1024, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TETylYwU-sIm"
      },
      "source": [
        "#  **costum classifier and  input embding convnet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tq0pUCWY9RZC",
        "outputId": "688e55d4-50aa-4c79-bc8a-10d7a3b89b01"
      },
      "source": [
        "dim_embd = 128\n",
        "time_stp = 40\n",
        "num_classes = 1\n",
        "mid_layer = 1024\n",
        "mid_drop = 0.35\n",
        "\n",
        "dim_embd = 128\n",
        "baseModel  =  timm.create_model('resnet26', pretrained=True , num_classes=dim_embd )\n",
        "\n",
        " \n",
        "classifier  = nn.Sequential(  nn.Linear(time_stp * dim_embd, mid_layer),\n",
        "                                nn.Dropout(mid_drop),\n",
        "                                nn.ReLU(),              \n",
        "            nn.Linear(mid_layer, num_classes)\n",
        "            )\n",
        "model_clsres = menoformer.DeVTr(base = baseModel , classifier= classifier, num_classes=num_classes , dim_embd=dim_embd , time_stp=time_stp)\n",
        "model_clsres"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): ResNet(\n",
              "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "        (layer1): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer2): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer3): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (layer4): Sequential(\n",
              "          (0): Bottleneck(\n",
              "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "            (downsample): Sequential(\n",
              "              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "              (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            )\n",
              "          )\n",
              "          (1): Bottleneck(\n",
              "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act1): ReLU(inplace=True)\n",
              "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act2): ReLU(inplace=True)\n",
              "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (act3): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "        (fc): Linear(in_features=2048, out_features=128, bias=True)\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "      (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=128, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=128, bias=True)\n",
              "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=5120, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Sequential(\n",
              "    (0): Linear(in_features=5120, out_features=1024, bias=True)\n",
              "    (1): Dropout(p=0.35, inplace=False)\n",
              "    (2): ReLU()\n",
              "    (3): Linear(in_features=1024, out_features=1, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDKGj4f-k6eJ",
        "outputId": "f1e6e24d-112c-4149-f2d1-b89f77573992"
      },
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.9.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PznlpQjjAb0J"
      },
      "source": [
        "# using  the helper dataset method of the package and the pre-traiend model on real data from kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRq0pZNKqIh0"
      },
      "source": [
        "! pip install -q kaggle\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtWKMhoh0TgK"
      },
      "source": [
        " from google.colab import files\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "AMjiklWl0YJF",
        "outputId": "b3455a69-2411-4810-ed7a-d6d15e32ed63"
      },
      "source": [
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-19e675b4-6966-4ad9-81ef-893c435b4289\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-19e675b4-6966-4ad9-81ef-893c435b4289\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"iraqai\",\"key\":\"42ec6bd05c8d3eaa132a15c4468d2081\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL3Iu1SR0eLQ"
      },
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeXa3DdX0ox4"
      },
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MdVkfuiu0rwf"
      },
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P389tPNg0wmI",
        "outputId": "51e962d7-e335-42b6-da4c-b0ae76bf5acd"
      },
      "source": [
        "\n",
        "! kaggle datasets list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "ref                                                         title                                              size  lastUpdated          downloadCount  \n",
            "----------------------------------------------------------  ------------------------------------------------  -----  -------------------  -------------  \n",
            "gpreda/reddit-vaccine-myths                                 Reddit Vaccine Myths                              235KB  2021-10-10 17:55:57          13524  \n",
            "crowww/a-large-scale-fish-dataset                           A Large Scale Fish Dataset                          3GB  2021-04-28 17:03:01           8121  \n",
            "imsparsh/musicnet-dataset                                   MusicNet Dataset                                   22GB  2021-02-18 14:12:19           3605  \n",
            "fatiimaezzahra/famous-iconic-women                          Famous Iconic Women                               838MB  2021-02-28 14:56:00           1550  \n",
            "dhruvildave/wikibooks-dataset                               Wikibooks Dataset                                   2GB  2021-07-03 18:37:20           3114  \n",
            "nickuzmenkov/nih-chest-xrays-tfrecords                      NIH Chest X-rays TFRecords                         11GB  2021-03-09 04:49:23           1301  \n",
            "promptcloud/careerbuilder-job-listing-2020                  Careerbuilder Job Listing 2020                     42MB  2021-03-05 06:59:52           2128  \n",
            "mathurinache/twitter-edge-nodes                             Twitter Edge Nodes                                342MB  2021-03-08 06:43:04           1038  \n",
            "coloradokb/dandelionimages                                  DandelionImages                                     4GB  2021-02-19 20:03:47            921  \n",
            "alsgroup/end-als                                            End ALS Kaggle Challenge                           12GB  2021-04-08 12:16:37            957  \n",
            "simiotic/github-code-snippets                               GitHub Code Snippets                                7GB  2021-03-03 11:34:39            378  \n",
            "stuartjames/lights                                          LightS: Light Specularity Dataset                  18GB  2021-02-18 14:32:26            170  \n",
            "mathurinache/the-lj-speech-dataset                          The LJ Speech Dataset                               3GB  2021-02-15 09:19:54            381  \n",
            "nickuzmenkov/ranzcr-clip-kfold-tfrecords                    RANZCR CLiP KFold TFRecords                         2GB  2021-02-21 13:29:51            138  \n",
            "imsparsh/accentdb-core-extended                             AccentDB - Core & Extended                          6GB  2021-02-17 14:22:54            153  \n",
            "landrykezebou/lvzhdr-tone-mapping-benchmark-dataset-tmonet  LVZ-HDR Tone Mapping Benchmark Dataset (TMO-Net)   24GB  2021-03-01 05:03:40            212  \n",
            "zynicide/wine-reviews                                       Wine Reviews                                       51MB  2017-11-27 17:08:04         145510  \n",
            "datasnaek/youtube-new                                       Trending YouTube Video Statistics                 201MB  2019-06-03 00:56:47         153751  \n",
            "datasnaek/chess                                             Chess Game Dataset (Lichess)                        3MB  2017-09-04 03:09:09          22780  \n",
            "residentmario/ramen-ratings                                 Ramen Ratings                                      40KB  2018-01-11 16:04:39          27969  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWVD5jIf0zbu",
        "outputId": "89e90bb4-7c31-4b23-f015-4a904006c0a4"
      },
      "source": [
        "!kaggle datasets download -d mohamedmustafa/real-life-violence-situations-dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading real-life-violence-situations-dataset.zip to /content\n",
            "100% 3.57G/3.58G [00:28<00:00, 77.1MB/s]\n",
            "100% 3.58G/3.58G [00:28<00:00, 135MB/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuXky2GF038E",
        "outputId": "b02b9fa9-93c9-42e8-c43d-7fa4fa305fc4"
      },
      "source": [
        "!unzip 'real-life-violence-situations-dataset'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  real-life-violence-situations-dataset.zip\n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_1.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_10.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_100.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_1000.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_101.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_102.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_103.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_104.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_105.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_106.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_107.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_108.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_109.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_11.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_110.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_111.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_112.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_113.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_114.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_115.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_116.mp4  \n",
            "  inflating: Real Life Violence Dataset/NonViolence/NV_117.mp4  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2lRLEsXAxn1",
        "outputId": "e0bc4701-9a86-43cd-effa-7ca024a8bed8"
      },
      "source": [
        "!ls -lia \"real life violence situations/Real Life Violence Dataset\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 80\n",
            "5373954 drwxr-xr-x 4 root root  4096 Oct 11 21:06 .\n",
            "5373953 drwxr-xr-x 3 root root  4096 Oct 11 21:06 ..\n",
            "5373955 drwxr-xr-x 2 root root 36864 Oct 11 21:06 NonViolence\n",
            "5374956 drwxr-xr-x 2 root root 36864 Oct 11 21:06 Violence\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWhM3zW_B1IQ"
      },
      "source": [
        "\n",
        "import time\n",
        "import os\n",
        "import time\n",
        "import random\n",
        "from datetime import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zn1JAhkrA4-E"
      },
      "source": [
        "def seed_everything(seed):\n",
        "    \"\"\"\n",
        "    Seeds basic parameters for reproductibility of results\n",
        "    \n",
        "    Arguments:\n",
        "        seed {int} -- Number of the seed\n",
        "    \"\"\"\n",
        "    random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "\n",
        "seed_everything(1001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VUU7RZp9By9u",
        "outputId": "ee4bb14c-31d4-4de2-9dd4-2174f67da2a0"
      },
      "source": [
        "torch.cuda.empty_cache()\n",
        "torch.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.9.0+cu111'"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FDIxl9FAB9Sy",
        "outputId": "7188176e-52a7-4689-ac47-dc1abfaec375"
      },
      "source": [
        "device =  'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda'"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb0UtrvACBVp"
      },
      "source": [
        "time_stp = 40\n",
        "RGB = 3\n",
        "H = 200\n",
        "W = 200"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "6Aj0dOzLCKZZ",
        "outputId": "cd3f4fef-d64f-41dd-c74b-48633c584193"
      },
      "source": [
        "def manual_count(filename):\n",
        "    i=0\n",
        "    vc = cv2.VideoCapture(filename)\n",
        "    if vc.isOpened():\n",
        "        rval , frame = vc.read()\n",
        "        if frame is None:\n",
        "            print(filename)\n",
        "            return False\n",
        "    else:\n",
        "        rval = False\n",
        "    i +=1\n",
        "    while i < 45:\n",
        "        rval, frame = vc.read()\n",
        "        if frame is None:\n",
        "            print(filename)\n",
        "            return False\n",
        "        i +=1\n",
        "\n",
        "    return True\n",
        "def path_list(main_dir,filename):\n",
        "    dk = []\n",
        "    label = []\n",
        "    i = 0\n",
        "    for x in os.listdir(main_dir):\n",
        "        print(x)\n",
        "        if 1 == 1:\n",
        "            td = main_dir+x+'/'\n",
        "            for file in os.listdir(td):\n",
        "                #print(td+file)\n",
        "                if manual_count(td+file):\n",
        "                    fl = os.path.join(td, file)\n",
        "                    dk.append(fl)\n",
        "                    if x == 'Violence':\n",
        "                        label.append(1)\n",
        "                    else:\n",
        "                        label.append(0)\n",
        "    df = pd.DataFrame(data={\"file\": dk, \"label\": label})\n",
        "    df.to_csv(filename, sep=',',index=False)\n",
        "    return filename\n",
        "path_list('real life violence situations/Real Life Violence Dataset/',\"./mtrain.csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NonViolence\n",
            "real life violence situations/Real Life Violence Dataset/NonViolence/NV_940.mp4\n",
            "real life violence situations/Real Life Violence Dataset/NonViolence/NV_354.mp4\n",
            "Violence\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'./mtrain.csv'"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "5WroPouJChra",
        "outputId": "4e8927bc-c919-4882-d009-1a5bca238f2c"
      },
      "source": [
        "df = pd.read_csv('./mtrain.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>real life violence situations/Real Life Violen...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>real life violence situations/Real Life Violen...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>real life violence situations/Real Life Violen...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>real life violence situations/Real Life Violen...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>real life violence situations/Real Life Violen...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                file  label\n",
              "0  real life violence situations/Real Life Violen...      0\n",
              "1  real life violence situations/Real Life Violen...      0\n",
              "2  real life violence situations/Real Life Violen...      0\n",
              "3  real life violence situations/Real Life Violen...      0\n",
              "4  real life violence situations/Real Life Violen...      0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOaAuQGdDL1G"
      },
      "source": [
        "train_df, valid_df = model_selection.train_test_split(\n",
        "    df, test_size=0.2, random_state=42\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJv2G1FKDowt"
      },
      "source": [
        "**using video dataset reader by our package it read the path of video and sample it and return dataset loader contain the video as tensor with it label and the video will be  subtracted to equal the number of frames passed to class**\n",
        "\n",
        "\n",
        "parameters is :\n",
        " \n",
        "\n",
        "1.   pandas dataframe contain the path and label of each video\n",
        "2.   number of frames for the  singal video\n",
        "3.   RGB is the number of color channles\n",
        "4.   h is the hieght of the frame for each video \n",
        "5.   w is the width of the frame for each video\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyLy0mAcDLyW"
      },
      "source": [
        "valid_dataset = vide_reader.TaskDataset(valid_df,timesep=time_stp,rgb=RGB,h=H,w=W)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjNljva7DLva"
      },
      "source": [
        "del df\n",
        "del train_df\n",
        "del valid_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbiM7RnjDLr3"
      },
      "source": [
        "BATCH_SIZE = 16\n",
        "val_BATCH_SIZE = 16\n",
        "valchecking = 1\n",
        "epoch = 7\n",
        "monit = 'val_loss'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taZwr20aGqqM"
      },
      "source": [
        "\n",
        "valid_loader =    DataLoader(  dataset=valid_dataset,\n",
        "        batch_size=val_BATCH_SIZE,pin_memory=True,\n",
        "        \n",
        "        drop_last=True,\n",
        "        num_workers=0,shuffle=False)\n",
        "dataloaders = {  'test':valid_loader}\n",
        "dataset_sizes = {  'test':len(valid_dataset) }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDvi7q0BGv7R",
        "outputId": "17e96763-d05a-4174-de55-86572a80cdd6"
      },
      "source": [
        "wight = 'drive/MyDrive/Colab Notebooks/transformers/violance-detaction-myresearch/vg19bn40convtransformer-ep-0.pth'\n",
        "model = menoformer.DeVTr(w= wight , base ='default')\n",
        "model.to(device)\n",
        "criterion = nn.BCEWithLogitsLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
        "sched =  lr_scheduler.ReduceLROnPlateau(optimizer,mode='min', factor=0.5, patience=2 , verbose=True)\n",
        "model.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): TimeWarp(\n",
              "    (baseModel): Sequential(\n",
              "      (0): VGG(\n",
              "        (features): Sequential(\n",
              "          (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (9): ReLU(inplace=True)\n",
              "          (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (12): ReLU(inplace=True)\n",
              "          (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (16): ReLU(inplace=True)\n",
              "          (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (19): ReLU(inplace=True)\n",
              "          (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (22): ReLU(inplace=True)\n",
              "          (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (25): ReLU(inplace=True)\n",
              "          (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (29): ReLU(inplace=True)\n",
              "          (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (32): ReLU(inplace=True)\n",
              "          (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (35): ReLU(inplace=True)\n",
              "          (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (38): ReLU(inplace=True)\n",
              "          (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (42): ReLU(inplace=True)\n",
              "          (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (45): ReLU(inplace=True)\n",
              "          (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (48): ReLU(inplace=True)\n",
              "          (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (51): ReLU(inplace=True)\n",
              "          (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (pre_logits): ConvMlp(\n",
              "          (fc1): Conv2d(512, 4096, kernel_size=(7, 7), stride=(1, 1))\n",
              "          (act1): ReLU(inplace=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "          (fc2): Conv2d(4096, 4096, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act2): ReLU(inplace=True)\n",
              "        )\n",
              "        (head): ClassifierHead(\n",
              "          (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
              "          (fc): Linear(in_features=4096, out_features=512, bias=True)\n",
              "          (flatten): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (1): PostionalEcnoder(\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (2): memoTransormer(\n",
              "    (encoder_layer): TransformerEncoderLayer(\n",
              "      (self_attn): MultiheadAttention(\n",
              "        (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "      )\n",
              "      (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "      (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout1): Dropout(p=0.1, inplace=False)\n",
              "      (dropout2): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer_encoder): TransformerEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (1): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (2): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (3): TransformerEncoderLayer(\n",
              "          (self_attn): MultiheadAttention(\n",
              "            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (dropout1): Dropout(p=0.1, inplace=False)\n",
              "          (dropout2): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (3): Flatten(start_dim=1, end_dim=-1)\n",
              "  (4): Linear(in_features=20480, out_features=1024, bias=True)\n",
              "  (5): Dropout(p=0.4, inplace=False)\n",
              "  (6): ReLU()\n",
              "  (7): Linear(in_features=1024, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cs310504HemV"
      },
      "source": [
        "def test_model(model, criteria, optimizer, device='cuda'):\n",
        "    model.to(device)\n",
        "    since = time.time()\n",
        "    model.eval()   # Set model to evaluate mode\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    pred_vs_real = {}\n",
        "    pred_vs_real['pred']= []\n",
        "    pred_vs_real['real']= []\n",
        "    tk0 = tqdm(dataloaders['test'], total=int(len(dataloaders['test'])))\n",
        "    counter = 0\n",
        "    for bi , sample in enumerate(tk0):\n",
        "        inputs = sample['video'].to(device , dtype=torch.float)\n",
        "        labels = sample['label'].view(sample['label'].shape[0],1)\n",
        "        labels = labels.to(device, dtype=torch.float)\n",
        "        outputs = model(inputs)\n",
        "        preds = torch.round(torch.sigmoid(outputs))\n",
        "        pred_vs_real['pred'].append(preds.cpu().detach().numpy())\n",
        "        pred_vs_real['real'].append(labels.cpu().detach().numpy())\n",
        "        loss = criterion(outputs, labels)\n",
        "        # statistics\n",
        "        counter += 1\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        tk0.set_postfix(loss=(running_loss / (counter * dataloaders['test'].batch_size )))\n",
        "    epoch_loss = running_loss / dataset_sizes['test']\n",
        "    epoch_acc = running_corrects.double() / dataset_sizes['test']\n",
        "    print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                'test', epoch_loss, epoch_acc))        \n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('testing complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "\n",
        "    return pred_vs_real "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tO2Gc_DNHxEL"
      },
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import time\n",
        "import json\n",
        "import copy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "7919030b52934d338a6efdc1eacff0db",
            "2781fa0ed67f4af8a069db7ef8646154",
            "cf2ca5c60a2649efa47a521b2d4508d9",
            "8b3e71ce037a409ebd0339070f3a240f",
            "327c06157b8342e3ba5acf2c412a0665",
            "984eead6c9684d179736037efbab6528",
            "eb47e4b0361a4b59abf17f963267c485",
            "4dd0abe4d2df4f8ea495dc5b9472f69c",
            "eb2c48031e5648088a9385dd869bcec1",
            "935d1e7e942b47d3973bdd67b627366a",
            "13c4fc51b961475487dd4f136e39acba"
          ]
        },
        "id": "1n1WgMkTH2Og",
        "outputId": "92d5fac3-a8a0-4cc2-f8d0-b1b6a0d7eb37"
      },
      "source": [
        "rl_vs_prd = test_model(model, criterion, optimizer, device)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7919030b52934d338a6efdc1eacff0db",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/25 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test Loss: 0.0834 Acc: 0.9750\n",
            "testing complete in 9m 45s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2orGwN6H7BX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}