Detection_SR_Recognition_drawtext.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Detection_SR_Recognition_drawtext.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Super Resolution 에 필요한 모듈 설치 & 정의\n",
        "- timm\n",
        "- SwinIR"
      ],
      "metadata": {
        "id": "4EhofnPtE4Jk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install timm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0m62UeB-a9G",
        "outputId": "727cfe30-9120-4d11-c54e-d61bfa8de7d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting timm\n",
            "  Downloading timm-0.5.4-py3-none-any.whl (431 kB)\n",
            "\u001b[K     |████████████████████████████████| 431 kB 6.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from timm) (1.11.0+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm) (0.12.0+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->timm) (4.1.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (2.23.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=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) (1.21.6)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->timm) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->timm) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->timm) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->timm) (2022.6.15)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.5.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SwinIR"
      ],
      "metadata": {
        "id": "WEAl1sXs94do"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# -----------------------------------------------------------------------------------\n",
        "# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257\n",
        "# Originally Written by Ze Liu, Modified by Jingyun Liang.\n",
        "# -----------------------------------------------------------------------------------\n",
        "\n",
        "import math\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.checkpoint as checkpoint\n",
        "\n",
        "from timm.models.layers import DropPath, to_2tuple, trunc_normal_\n",
        "\n",
        "\n",
        "class Mlp(nn.Module):\n",
        "    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n",
        "        super().__init__()\n",
        "        out_features = out_features or in_features\n",
        "        hidden_features = hidden_features or in_features\n",
        "        self.fc1 = nn.Linear(in_features, hidden_features)\n",
        "        self.act = act_layer()\n",
        "        self.fc2 = nn.Linear(hidden_features, out_features)\n",
        "        self.drop = nn.Dropout(drop)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc1(x)\n",
        "        x = self.act(x)\n",
        "        x = self.drop(x)\n",
        "        x = self.fc2(x)\n",
        "        x = self.drop(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "def window_partition(x, window_size):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "        x: (B, H, W, C)\n",
        "        window_size (int): window size\n",
        "\n",
        "    Returns:\n",
        "        windows: (num_windows*B, window_size, window_size, C)\n",
        "    \"\"\"\n",
        "    B, H, W, C = x.shape\n",
        "    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n",
        "    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n",
        "    return windows\n",
        "\n",
        "\n",
        "def window_reverse(windows, window_size, H, W):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "        windows: (num_windows*B, window_size, window_size, C)\n",
        "        window_size (int): Window size\n",
        "        H (int): Height of image\n",
        "        W (int): Width of image\n",
        "\n",
        "    Returns:\n",
        "        x: (B, H, W, C)\n",
        "    \"\"\"\n",
        "    B = int(windows.shape[0] / (H * W / window_size / window_size))\n",
        "    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n",
        "    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n",
        "    return x\n",
        "\n",
        "\n",
        "class WindowAttention(nn.Module):\n",
        "    r\"\"\" Window based multi-head self attention (W-MSA) module with relative position bias.\n",
        "    It supports both of shifted and non-shifted window.\n",
        "\n",
        "    Args:\n",
        "        dim (int): Number of input channels.\n",
        "        window_size (tuple[int]): The height and width of the window.\n",
        "        num_heads (int): Number of attention heads.\n",
        "        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n",
        "        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n",
        "        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n",
        "        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):\n",
        "\n",
        "        super().__init__()\n",
        "        self.dim = dim\n",
        "        self.window_size = window_size  # Wh, Ww\n",
        "        self.num_heads = num_heads\n",
        "        head_dim = dim // num_heads\n",
        "        self.scale = qk_scale or head_dim ** -0.5\n",
        "\n",
        "        # define a parameter table of relative position bias\n",
        "        self.relative_position_bias_table = nn.Parameter(\n",
        "            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH\n",
        "\n",
        "        # get pair-wise relative position index for each token inside the window\n",
        "        coords_h = torch.arange(self.window_size[0])\n",
        "        coords_w = torch.arange(self.window_size[1])\n",
        "        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n",
        "        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n",
        "        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww\n",
        "        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2\n",
        "        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0\n",
        "        relative_coords[:, :, 1] += self.window_size[1] - 1\n",
        "        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n",
        "        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n",
        "        self.register_buffer(\"relative_position_index\", relative_position_index)\n",
        "\n",
        "        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n",
        "        self.attn_drop = nn.Dropout(attn_drop)\n",
        "        self.proj = nn.Linear(dim, dim)\n",
        "\n",
        "        self.proj_drop = nn.Dropout(proj_drop)\n",
        "\n",
        "        trunc_normal_(self.relative_position_bias_table, std=.02)\n",
        "        self.softmax = nn.Softmax(dim=-1)\n",
        "\n",
        "    def forward(self, x, mask=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            x: input features with shape of (num_windows*B, N, C)\n",
        "            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n",
        "        \"\"\"\n",
        "        B_, N, C = x.shape\n",
        "        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n",
        "        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n",
        "\n",
        "        q = q * self.scale\n",
        "        attn = (q @ k.transpose(-2, -1))\n",
        "\n",
        "        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(\n",
        "            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH\n",
        "        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww\n",
        "        attn = attn + relative_position_bias.unsqueeze(0)\n",
        "\n",
        "        if mask is not None:\n",
        "            nW = mask.shape[0]\n",
        "            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n",
        "            attn = attn.view(-1, self.num_heads, N, N)\n",
        "            attn = self.softmax(attn)\n",
        "        else:\n",
        "            attn = self.softmax(attn)\n",
        "\n",
        "        attn = self.attn_drop(attn)\n",
        "\n",
        "        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n",
        "        x = self.proj(x)\n",
        "        x = self.proj_drop(x)\n",
        "        return x\n",
        "\n",
        "    def extra_repr(self) -> str:\n",
        "        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'\n",
        "\n",
        "    def flops(self, N):\n",
        "        # calculate flops for 1 window with token length of N\n",
        "        flops = 0\n",
        "        # qkv = self.qkv(x)\n",
        "        flops += N * self.dim * 3 * self.dim\n",
        "        # attn = (q @ k.transpose(-2, -1))\n",
        "        flops += self.num_heads * N * (self.dim // self.num_heads) * N\n",
        "        #  x = (attn @ v)\n",
        "        flops += self.num_heads * N * N * (self.dim // self.num_heads)\n",
        "        # x = self.proj(x)\n",
        "        flops += N * self.dim * self.dim\n",
        "        return flops\n",
        "\n",
        "\n",
        "class SwinTransformerBlock(nn.Module):\n",
        "    r\"\"\" Swin Transformer Block.\n",
        "\n",
        "    Args:\n",
        "        dim (int): Number of input channels.\n",
        "        input_resolution (tuple[int]): Input resulotion.\n",
        "        num_heads (int): Number of attention heads.\n",
        "        window_size (int): Window size.\n",
        "        shift_size (int): Shift size for SW-MSA.\n",
        "        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n",
        "        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n",
        "        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n",
        "        drop (float, optional): Dropout rate. Default: 0.0\n",
        "        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n",
        "        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n",
        "        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n",
        "        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,\n",
        "                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n",
        "                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):\n",
        "        super().__init__()\n",
        "        self.dim = dim\n",
        "        self.input_resolution = input_resolution\n",
        "        self.num_heads = num_heads\n",
        "        self.window_size = window_size\n",
        "        self.shift_size = shift_size\n",
        "        self.mlp_ratio = mlp_ratio\n",
        "        if min(self.input_resolution) <= self.window_size:\n",
        "            # if window size is larger than input resolution, we don't partition windows\n",
        "            self.shift_size = 0\n",
        "            self.window_size = min(self.input_resolution)\n",
        "        assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n",
        "\n",
        "        self.norm1 = norm_layer(dim)\n",
        "        self.attn = WindowAttention(\n",
        "            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,\n",
        "            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n",
        "\n",
        "        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n",
        "        self.norm2 = norm_layer(dim)\n",
        "        mlp_hidden_dim = int(dim * mlp_ratio)\n",
        "        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n",
        "\n",
        "        if self.shift_size > 0:\n",
        "            attn_mask = self.calculate_mask(self.input_resolution)\n",
        "        else:\n",
        "            attn_mask = None\n",
        "\n",
        "        self.register_buffer(\"attn_mask\", attn_mask)\n",
        "\n",
        "    def calculate_mask(self, x_size):\n",
        "        # calculate attention mask for SW-MSA\n",
        "        H, W = x_size\n",
        "        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1\n",
        "        h_slices = (slice(0, -self.window_size),\n",
        "                    slice(-self.window_size, -self.shift_size),\n",
        "                    slice(-self.shift_size, None))\n",
        "        w_slices = (slice(0, -self.window_size),\n",
        "                    slice(-self.window_size, -self.shift_size),\n",
        "                    slice(-self.shift_size, None))\n",
        "        cnt = 0\n",
        "        for h in h_slices:\n",
        "            for w in w_slices:\n",
        "                img_mask[:, h, w, :] = cnt\n",
        "                cnt += 1\n",
        "\n",
        "        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1\n",
        "        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n",
        "        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n",
        "        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))\n",
        "\n",
        "        return attn_mask\n",
        "\n",
        "    def forward(self, x, x_size):\n",
        "        H, W = x_size\n",
        "        B, L, C = x.shape\n",
        "        # assert L == H * W, \"input feature has wrong size\"\n",
        "\n",
        "        shortcut = x\n",
        "        x = self.norm1(x)\n",
        "        x = x.view(B, H, W, C)\n",
        "\n",
        "        # cyclic shift\n",
        "        if self.shift_size > 0:\n",
        "            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n",
        "        else:\n",
        "            shifted_x = x\n",
        "\n",
        "        # partition windows\n",
        "        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C\n",
        "        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C\n",
        "\n",
        "        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size\n",
        "        if self.input_resolution == x_size:\n",
        "            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C\n",
        "        else:\n",
        "            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))\n",
        "\n",
        "        # merge windows\n",
        "        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n",
        "        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C\n",
        "\n",
        "        # reverse cyclic shift\n",
        "        if self.shift_size > 0:\n",
        "            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n",
        "        else:\n",
        "            x = shifted_x\n",
        "        x = x.view(B, H * W, C)\n",
        "\n",
        "        # FFN\n",
        "        x = shortcut + self.drop_path(x)\n",
        "        x = x + self.drop_path(self.mlp(self.norm2(x)))\n",
        "\n",
        "        return x\n",
        "\n",
        "    def extra_repr(self) -> str:\n",
        "        return f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \" \\\n",
        "               f\"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}\"\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        H, W = self.input_resolution\n",
        "        # norm1\n",
        "        flops += self.dim * H * W\n",
        "        # W-MSA/SW-MSA\n",
        "        nW = H * W / self.window_size / self.window_size\n",
        "        flops += nW * self.attn.flops(self.window_size * self.window_size)\n",
        "        # mlp\n",
        "        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio\n",
        "        # norm2\n",
        "        flops += self.dim * H * W\n",
        "        return flops\n",
        "\n",
        "\n",
        "class PatchMerging(nn.Module):\n",
        "    r\"\"\" Patch Merging Layer.\n",
        "\n",
        "    Args:\n",
        "        input_resolution (tuple[int]): Resolution of input feature.\n",
        "        dim (int): Number of input channels.\n",
        "        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):\n",
        "        super().__init__()\n",
        "        self.input_resolution = input_resolution\n",
        "        self.dim = dim\n",
        "        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n",
        "        self.norm = norm_layer(4 * dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        x: B, H*W, C\n",
        "        \"\"\"\n",
        "        H, W = self.input_resolution\n",
        "        B, L, C = x.shape\n",
        "        assert L == H * W, \"input feature has wrong size\"\n",
        "        assert H % 2 == 0 and W % 2 == 0, f\"x size ({H}*{W}) are not even.\"\n",
        "\n",
        "        x = x.view(B, H, W, C)\n",
        "\n",
        "        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\n",
        "        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\n",
        "        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\n",
        "        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\n",
        "        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C\n",
        "        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C\n",
        "\n",
        "        x = self.norm(x)\n",
        "        x = self.reduction(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def extra_repr(self) -> str:\n",
        "        return f\"input_resolution={self.input_resolution}, dim={self.dim}\"\n",
        "\n",
        "    def flops(self):\n",
        "        H, W = self.input_resolution\n",
        "        flops = H * W * self.dim\n",
        "        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim\n",
        "        return flops\n",
        "\n",
        "\n",
        "class BasicLayer(nn.Module):\n",
        "    \"\"\" A basic Swin Transformer layer for one stage.\n",
        "\n",
        "    Args:\n",
        "        dim (int): Number of input channels.\n",
        "        input_resolution (tuple[int]): Input resolution.\n",
        "        depth (int): Number of blocks.\n",
        "        num_heads (int): Number of attention heads.\n",
        "        window_size (int): Local window size.\n",
        "        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n",
        "        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n",
        "        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n",
        "        drop (float, optional): Dropout rate. Default: 0.0\n",
        "        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n",
        "        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n",
        "        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n",
        "        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n",
        "        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dim, input_resolution, depth, num_heads, window_size,\n",
        "                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n",
        "                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):\n",
        "\n",
        "        super().__init__()\n",
        "        self.dim = dim\n",
        "        self.input_resolution = input_resolution\n",
        "        self.depth = depth\n",
        "        self.use_checkpoint = use_checkpoint\n",
        "\n",
        "        # build blocks\n",
        "        self.blocks = nn.ModuleList([\n",
        "            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,\n",
        "                                 num_heads=num_heads, window_size=window_size,\n",
        "                                 shift_size=0 if (i % 2 == 0) else window_size // 2,\n",
        "                                 mlp_ratio=mlp_ratio,\n",
        "                                 qkv_bias=qkv_bias, qk_scale=qk_scale,\n",
        "                                 drop=drop, attn_drop=attn_drop,\n",
        "                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n",
        "                                 norm_layer=norm_layer)\n",
        "            for i in range(depth)])\n",
        "\n",
        "        # patch merging layer\n",
        "        if downsample is not None:\n",
        "            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)\n",
        "        else:\n",
        "            self.downsample = None\n",
        "\n",
        "    def forward(self, x, x_size):\n",
        "        for blk in self.blocks:\n",
        "            if self.use_checkpoint:\n",
        "                x = checkpoint.checkpoint(blk, x, x_size)\n",
        "            else:\n",
        "                x = blk(x, x_size)\n",
        "        if self.downsample is not None:\n",
        "            x = self.downsample(x)\n",
        "        return x\n",
        "\n",
        "    def extra_repr(self) -> str:\n",
        "        return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        for blk in self.blocks:\n",
        "            flops += blk.flops()\n",
        "        if self.downsample is not None:\n",
        "            flops += self.downsample.flops()\n",
        "        return flops\n",
        "\n",
        "\n",
        "class RSTB(nn.Module):\n",
        "    \"\"\"Residual Swin Transformer Block (RSTB).\n",
        "\n",
        "    Args:\n",
        "        dim (int): Number of input channels.\n",
        "        input_resolution (tuple[int]): Input resolution.\n",
        "        depth (int): Number of blocks.\n",
        "        num_heads (int): Number of attention heads.\n",
        "        window_size (int): Local window size.\n",
        "        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n",
        "        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n",
        "        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n",
        "        drop (float, optional): Dropout rate. Default: 0.0\n",
        "        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n",
        "        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n",
        "        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n",
        "        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n",
        "        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n",
        "        img_size: Input image size.\n",
        "        patch_size: Patch size.\n",
        "        resi_connection: The convolutional block before residual connection.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dim, input_resolution, depth, num_heads, window_size,\n",
        "                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n",
        "                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,\n",
        "                 img_size=224, patch_size=4, resi_connection='1conv'):\n",
        "        super(RSTB, self).__init__()\n",
        "\n",
        "        self.dim = dim\n",
        "        self.input_resolution = input_resolution\n",
        "\n",
        "        self.residual_group = BasicLayer(dim=dim,\n",
        "                                         input_resolution=input_resolution,\n",
        "                                         depth=depth,\n",
        "                                         num_heads=num_heads,\n",
        "                                         window_size=window_size,\n",
        "                                         mlp_ratio=mlp_ratio,\n",
        "                                         qkv_bias=qkv_bias, qk_scale=qk_scale,\n",
        "                                         drop=drop, attn_drop=attn_drop,\n",
        "                                         drop_path=drop_path,\n",
        "                                         norm_layer=norm_layer,\n",
        "                                         downsample=downsample,\n",
        "                                         use_checkpoint=use_checkpoint)\n",
        "\n",
        "        if resi_connection == '1conv':\n",
        "            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n",
        "        elif resi_connection == '3conv':\n",
        "            # to save parameters and memory\n",
        "            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),\n",
        "                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))\n",
        "\n",
        "        self.patch_embed = PatchEmbed(\n",
        "            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,\n",
        "            norm_layer=None)\n",
        "\n",
        "        self.patch_unembed = PatchUnEmbed(\n",
        "            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,\n",
        "            norm_layer=None)\n",
        "\n",
        "    def forward(self, x, x_size):\n",
        "        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        flops += self.residual_group.flops()\n",
        "        H, W = self.input_resolution\n",
        "        flops += H * W * self.dim * self.dim * 9\n",
        "        flops += self.patch_embed.flops()\n",
        "        flops += self.patch_unembed.flops()\n",
        "\n",
        "        return flops\n",
        "\n",
        "\n",
        "class PatchEmbed(nn.Module):\n",
        "    r\"\"\" Image to Patch Embedding\n",
        "\n",
        "    Args:\n",
        "        img_size (int): Image size.  Default: 224.\n",
        "        patch_size (int): Patch token size. Default: 4.\n",
        "        in_chans (int): Number of input image channels. Default: 3.\n",
        "        embed_dim (int): Number of linear projection output channels. Default: 96.\n",
        "        norm_layer (nn.Module, optional): Normalization layer. Default: None\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n",
        "        super().__init__()\n",
        "        img_size = to_2tuple(img_size)\n",
        "        patch_size = to_2tuple(patch_size)\n",
        "        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]\n",
        "        self.img_size = img_size\n",
        "        self.patch_size = patch_size\n",
        "        self.patches_resolution = patches_resolution\n",
        "        self.num_patches = patches_resolution[0] * patches_resolution[1]\n",
        "\n",
        "        self.in_chans = in_chans\n",
        "        self.embed_dim = embed_dim\n",
        "\n",
        "        if norm_layer is not None:\n",
        "            self.norm = norm_layer(embed_dim)\n",
        "        else:\n",
        "            self.norm = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C\n",
        "        if self.norm is not None:\n",
        "            x = self.norm(x)\n",
        "        return x\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        H, W = self.img_size\n",
        "        if self.norm is not None:\n",
        "            flops += H * W * self.embed_dim\n",
        "        return flops\n",
        "\n",
        "\n",
        "class PatchUnEmbed(nn.Module):\n",
        "    r\"\"\" Image to Patch Unembedding\n",
        "\n",
        "    Args:\n",
        "        img_size (int): Image size.  Default: 224.\n",
        "        patch_size (int): Patch token size. Default: 4.\n",
        "        in_chans (int): Number of input image channels. Default: 3.\n",
        "        embed_dim (int): Number of linear projection output channels. Default: 96.\n",
        "        norm_layer (nn.Module, optional): Normalization layer. Default: None\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n",
        "        super().__init__()\n",
        "        img_size = to_2tuple(img_size)\n",
        "        patch_size = to_2tuple(patch_size)\n",
        "        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]\n",
        "        self.img_size = img_size\n",
        "        self.patch_size = patch_size\n",
        "        self.patches_resolution = patches_resolution\n",
        "        self.num_patches = patches_resolution[0] * patches_resolution[1]\n",
        "\n",
        "        self.in_chans = in_chans\n",
        "        self.embed_dim = embed_dim\n",
        "\n",
        "    def forward(self, x, x_size):\n",
        "        B, HW, C = x.shape\n",
        "        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C\n",
        "        return x\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        return flops\n",
        "\n",
        "\n",
        "class Upsample(nn.Sequential):\n",
        "    \"\"\"Upsample module.\n",
        "\n",
        "    Args:\n",
        "        scale (int): Scale factor. Supported scales: 2^n and 3.\n",
        "        num_feat (int): Channel number of intermediate features.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, scale, num_feat):\n",
        "        m = []\n",
        "        if (scale & (scale - 1)) == 0:  # scale = 2^n\n",
        "            for _ in range(int(math.log(scale, 2))):\n",
        "                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n",
        "                m.append(nn.PixelShuffle(2))\n",
        "        elif scale == 3:\n",
        "            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n",
        "            m.append(nn.PixelShuffle(3))\n",
        "        else:\n",
        "            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')\n",
        "        super(Upsample, self).__init__(*m)\n",
        "\n",
        "\n",
        "class UpsampleOneStep(nn.Sequential):\n",
        "    \"\"\"UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)\n",
        "       Used in lightweight SR to save parameters.\n",
        "\n",
        "    Args:\n",
        "        scale (int): Scale factor. Supported scales: 2^n and 3.\n",
        "        num_feat (int): Channel number of intermediate features.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):\n",
        "        self.num_feat = num_feat\n",
        "        self.input_resolution = input_resolution\n",
        "        m = []\n",
        "        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))\n",
        "        m.append(nn.PixelShuffle(scale))\n",
        "        super(UpsampleOneStep, self).__init__(*m)\n",
        "\n",
        "    def flops(self):\n",
        "        H, W = self.input_resolution\n",
        "        flops = H * W * self.num_feat * 3 * 9\n",
        "        return flops\n",
        "\n",
        "# 이거 사용\n",
        "# PatchEmbed, RSTB\n",
        "class SwinIR(nn.Module):\n",
        "    r\"\"\" SwinIR\n",
        "        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.\n",
        "\n",
        "    Args:\n",
        "        img_size (int | tuple(int)): Input image size. Default 64\n",
        "        patch_size (int | tuple(int)): Patch size. Default: 1\n",
        "        in_chans (int): Number of input image channels. Default: 3\n",
        "        embed_dim (int): Patch embedding dimension. Default: 96\n",
        "        depths (tuple(int)): Depth of each Swin Transformer layer.\n",
        "        num_heads (tuple(int)): Number of attention heads in different layers.\n",
        "        window_size (int): Window size. Default: 7\n",
        "        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n",
        "        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n",
        "        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None\n",
        "        drop_rate (float): Dropout rate. Default: 0\n",
        "        attn_drop_rate (float): Attention dropout rate. Default: 0\n",
        "        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n",
        "        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n",
        "        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False\n",
        "        patch_norm (bool): If True, add normalization after patch embedding. Default: True\n",
        "        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False\n",
        "        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction\n",
        "        img_range: Image range. 1. or 255.\n",
        "        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None\n",
        "        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, img_size=64, patch_size=1, in_chans=3,\n",
        "                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],\n",
        "                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,\n",
        "                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,\n",
        "                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,\n",
        "                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',\n",
        "                 **kwargs):\n",
        "        super(SwinIR, self).__init__()\n",
        "        num_in_ch = in_chans\n",
        "        num_out_ch = in_chans\n",
        "        num_feat = 64\n",
        "        self.img_range = img_range\n",
        "        if in_chans == 3:\n",
        "            rgb_mean = (0.4488, 0.4371, 0.4040)\n",
        "            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n",
        "        else:\n",
        "            self.mean = torch.zeros(1, 1, 1, 1)\n",
        "        self.upscale = upscale\n",
        "        self.upsampler = upsampler\n",
        "        self.window_size = window_size\n",
        "\n",
        "        #####################################################################################################\n",
        "        ################################### 1, shallow feature extraction ###################################\n",
        "        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n",
        "\n",
        "        #####################################################################################################\n",
        "        ################################### 2, deep feature extraction ######################################\n",
        "        self.num_layers = len(depths)\n",
        "        self.embed_dim = embed_dim\n",
        "        self.ape = ape\n",
        "        self.patch_norm = patch_norm\n",
        "        self.num_features = embed_dim\n",
        "        self.mlp_ratio = mlp_ratio\n",
        "\n",
        "        # split image into non-overlapping patches\n",
        "        self.patch_embed = PatchEmbed(\n",
        "            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,\n",
        "            norm_layer=norm_layer if self.patch_norm else None)\n",
        "        num_patches = self.patch_embed.num_patches\n",
        "        patches_resolution = self.patch_embed.patches_resolution\n",
        "        self.patches_resolution = patches_resolution\n",
        "\n",
        "        # merge non-overlapping patches into image\n",
        "        self.patch_unembed = PatchUnEmbed(\n",
        "            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,\n",
        "            norm_layer=norm_layer if self.patch_norm else None)\n",
        "\n",
        "        # absolute position embedding\n",
        "        if self.ape:\n",
        "            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))\n",
        "            trunc_normal_(self.absolute_pos_embed, std=.02)\n",
        "\n",
        "        self.pos_drop = nn.Dropout(p=drop_rate)\n",
        "\n",
        "        # stochastic depth\n",
        "        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule\n",
        "\n",
        "        # build Residual Swin Transformer blocks (RSTB)\n",
        "        self.layers = nn.ModuleList()\n",
        "        for i_layer in range(self.num_layers):\n",
        "            layer = RSTB(dim=embed_dim,\n",
        "                         input_resolution=(patches_resolution[0],\n",
        "                                           patches_resolution[1]),\n",
        "                         depth=depths[i_layer],\n",
        "                         num_heads=num_heads[i_layer],\n",
        "                         window_size=window_size,\n",
        "                         mlp_ratio=self.mlp_ratio,\n",
        "                         qkv_bias=qkv_bias, qk_scale=qk_scale,\n",
        "                         drop=drop_rate, attn_drop=attn_drop_rate,\n",
        "                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results\n",
        "                         norm_layer=norm_layer,\n",
        "                         downsample=None,\n",
        "                         use_checkpoint=use_checkpoint,\n",
        "                         img_size=img_size,\n",
        "                         patch_size=patch_size,\n",
        "                         resi_connection=resi_connection\n",
        "\n",
        "                         )\n",
        "            self.layers.append(layer)\n",
        "        self.norm = norm_layer(self.num_features)\n",
        "\n",
        "        # build the last conv layer in deep feature extraction\n",
        "        if resi_connection == '1conv':\n",
        "            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n",
        "        elif resi_connection == '3conv':\n",
        "            # to save parameters and memory\n",
        "            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),\n",
        "                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),\n",
        "                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))\n",
        "\n",
        "        #####################################################################################################\n",
        "        ################################ 3, high quality image reconstruction ################################\n",
        "        if self.upsampler == 'pixelshuffle':\n",
        "            # for classical SR\n",
        "            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),\n",
        "                                                      nn.LeakyReLU(inplace=True))\n",
        "            self.upsample = Upsample(upscale, num_feat)\n",
        "            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n",
        "        elif self.upsampler == 'pixelshuffledirect':\n",
        "            # for lightweight SR (to save parameters)\n",
        "            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,\n",
        "                                            (patches_resolution[0], patches_resolution[1]))\n",
        "        elif self.upsampler == 'nearest+conv':\n",
        "            # for real-world SR (less artifacts)\n",
        "            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),\n",
        "                                                      nn.LeakyReLU(inplace=True))\n",
        "            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n",
        "            if self.upscale == 4:\n",
        "                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n",
        "            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n",
        "            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n",
        "            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
        "        else:\n",
        "            # for image denoising and JPEG compression artifact reduction\n",
        "            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)\n",
        "\n",
        "        self.apply(self._init_weights)\n",
        "\n",
        "    def _init_weights(self, m):\n",
        "        if isinstance(m, nn.Linear):\n",
        "            trunc_normal_(m.weight, std=.02)\n",
        "            if isinstance(m, nn.Linear) and m.bias is not None:\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "        elif isinstance(m, nn.LayerNorm):\n",
        "            nn.init.constant_(m.bias, 0)\n",
        "            nn.init.constant_(m.weight, 1.0)\n",
        "\n",
        "    @torch.jit.ignore\n",
        "    def no_weight_decay(self):\n",
        "        return {'absolute_pos_embed'}\n",
        "\n",
        "    @torch.jit.ignore\n",
        "    def no_weight_decay_keywords(self):\n",
        "        return {'relative_position_bias_table'}\n",
        "\n",
        "    def check_image_size(self, x):\n",
        "        _, _, h, w = x.size()\n",
        "        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size\n",
        "        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size\n",
        "        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')\n",
        "        return x\n",
        "\n",
        "    def forward_features(self, x):\n",
        "        x_size = (x.shape[2], x.shape[3])\n",
        "        x = self.patch_embed(x)\n",
        "        if self.ape:\n",
        "            x = x + self.absolute_pos_embed\n",
        "        x = self.pos_drop(x)\n",
        "\n",
        "        for layer in self.layers:\n",
        "            x = layer(x, x_size)\n",
        "\n",
        "        x = self.norm(x)  # B L C\n",
        "        x = self.patch_unembed(x, x_size)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def forward(self, x):\n",
        "        H, W = x.shape[2:]\n",
        "        x = self.check_image_size(x)\n",
        "        \n",
        "        self.mean = self.mean.type_as(x)\n",
        "        x = (x - self.mean) * self.img_range\n",
        "\n",
        "        if self.upsampler == 'pixelshuffle':\n",
        "            # for classical SR\n",
        "            x = self.conv_first(x)\n",
        "            x = self.conv_after_body(self.forward_features(x)) + x\n",
        "            x = self.conv_before_upsample(x)\n",
        "            x = self.conv_last(self.upsample(x))\n",
        "        elif self.upsampler == 'pixelshuffledirect':\n",
        "            # for lightweight SR\n",
        "            x = self.conv_first(x)\n",
        "            x = self.conv_after_body(self.forward_features(x)) + x\n",
        "            x = self.upsample(x)\n",
        "        elif self.upsampler == 'nearest+conv':\n",
        "            # for real-world SR\n",
        "            x = self.conv_first(x)\n",
        "            x = self.conv_after_body(self.forward_features(x)) + x\n",
        "            x = self.conv_before_upsample(x)\n",
        "            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))\n",
        "            if self.upscale == 4:\n",
        "                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))\n",
        "            x = self.conv_last(self.lrelu(self.conv_hr(x)))\n",
        "        else:\n",
        "            # for image denoising and JPEG compression artifact reduction\n",
        "            x_first = self.conv_first(x)\n",
        "            res = self.conv_after_body(self.forward_features(x_first)) + x_first\n",
        "            x = x + self.conv_last(res)\n",
        "\n",
        "        x = x / self.img_range + self.mean\n",
        "\n",
        "        return x[:, :, :H*self.upscale, :W*self.upscale]\n",
        "\n",
        "    def flops(self):\n",
        "        flops = 0\n",
        "        H, W = self.patches_resolution\n",
        "        flops += H * W * 3 * self.embed_dim * 9\n",
        "        flops += self.patch_embed.flops()\n",
        "        for i, layer in enumerate(self.layers):\n",
        "            flops += layer.flops()\n",
        "        flops += H * W * 3 * self.embed_dim * self.embed_dim\n",
        "        flops += self.upsample.flops()\n",
        "        return flops\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    upscale = 4\n",
        "    window_size = 8\n",
        "    height = (1024 // upscale // window_size + 1) * window_size\n",
        "    width = (720 // upscale // window_size + 1) * window_size\n",
        "    model = SwinIR(upscale=2, img_size=(height, width),\n",
        "                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],\n",
        "                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')\n",
        "    print(model)\n",
        "    print(height, width, model.flops() / 1e9)\n",
        "\n",
        "    x = torch.randn((1, 3, height, width))\n",
        "    x = model(x)\n",
        "    print(x.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0zUKWAN96dC",
        "outputId": "ee911866-be4d-4c19-93bc-226c7d446457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SwinIR(\n",
            "  (conv_first): Conv2d(3, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (patch_embed): PatchEmbed(\n",
            "    (norm): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (patch_unembed): PatchUnEmbed()\n",
            "  (pos_drop): Dropout(p=0.0, inplace=False)\n",
            "  (layers): ModuleList(\n",
            "    (0): RSTB(\n",
            "      (residual_group): BasicLayer(\n",
            "        dim=60, input_resolution=(264, 184), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): Identity()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (conv): Conv2d(60, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (patch_embed): PatchEmbed()\n",
            "      (patch_unembed): PatchUnEmbed()\n",
            "    )\n",
            "    (1): RSTB(\n",
            "      (residual_group): BasicLayer(\n",
            "        dim=60, input_resolution=(264, 184), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (conv): Conv2d(60, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (patch_embed): PatchEmbed()\n",
            "      (patch_unembed): PatchUnEmbed()\n",
            "    )\n",
            "    (2): RSTB(\n",
            "      (residual_group): BasicLayer(\n",
            "        dim=60, input_resolution=(264, 184), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (conv): Conv2d(60, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (patch_embed): PatchEmbed()\n",
            "      (patch_unembed): PatchUnEmbed()\n",
            "    )\n",
            "    (3): RSTB(\n",
            "      (residual_group): BasicLayer(\n",
            "        dim=60, input_resolution=(264, 184), depth=6\n",
            "        (blocks): ModuleList(\n",
            "          (0): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (1): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (2): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (3): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (4): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (5): SwinTransformerBlock(\n",
            "            dim=60, input_resolution=(264, 184), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2\n",
            "            (norm1): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): WindowAttention(\n",
            "              dim=60, window_size=(8, 8), num_heads=6\n",
            "              (qkv): Linear(in_features=60, out_features=180, bias=True)\n",
            "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
            "              (proj): Linear(in_features=60, out_features=60, bias=True)\n",
            "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
            "              (softmax): Softmax(dim=-1)\n",
            "            )\n",
            "            (drop_path): DropPath()\n",
            "            (norm2): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): Mlp(\n",
            "              (fc1): Linear(in_features=60, out_features=120, bias=True)\n",
            "              (act): GELU()\n",
            "              (fc2): Linear(in_features=120, out_features=60, bias=True)\n",
            "              (drop): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (conv): Conv2d(60, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (patch_embed): PatchEmbed()\n",
            "      (patch_unembed): PatchUnEmbed()\n",
            "    )\n",
            "  )\n",
            "  (norm): LayerNorm((60,), eps=1e-05, elementwise_affine=True)\n",
            "  (conv_after_body): Conv2d(60, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (upsample): UpsampleOneStep(\n",
            "    (0): Conv2d(60, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): PixelShuffle(upscale_factor=2)\n",
            "  )\n",
            ")\n",
            "264 184 49.6495296\n",
            "torch.Size([1, 3, 528, 368])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 함수화"
      ],
      "metadata": {
        "id": "k4PyisJdQGVp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "# model_path = '/content/drive/MyDrive/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth'\n",
        "\n",
        "def sr(image, device = torch.device('cuda' if torch.cuda.is_available() else 'cpu'), scale = 4, model_path = None):\n",
        "\n",
        "    if model_path is None:\n",
        "      \n",
        "      url = \"https://drive.google.com/uc?id=1d1RwNhiyxVu7zkNcIReifcyg5wkVc-xv\"\n",
        "      output = \"003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth\"\n",
        "\n",
        "      if not os.path.exists('/content/'+output):\n",
        "        \n",
        "        model_path = gdown.download(url, output, quiet=False)\n",
        "      model_path = '/content/'+output\n",
        "      \n",
        "        \n",
        "    else:\n",
        "      model_path = model_path\n",
        "\n",
        "    img_lq = image.astype(np.float32) / 255.\n",
        "    img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]], (2, 0, 1))  # HCW-BGR to CHW-RGB\n",
        "    img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device)  # CHW-RGB to NCHW-RGB\n",
        "\n",
        "    \n",
        "    \n",
        "    model = SwinIR(upscale=scale, in_chans=3, img_size=64, window_size=8,\n",
        "                            img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,\n",
        "                            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],\n",
        "                            mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')\n",
        "    param_key_g = 'params_ema'\n",
        "\n",
        "    pretrained_model = torch.load(model_path)\n",
        "    model.load_state_dict(pretrained_model[param_key_g] if param_key_g in pretrained_model.keys() else pretrained_model, strict=True)\n",
        "\n",
        "    model.eval()\n",
        "    model = model.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        # pad input image to be a multiple of window_size\n",
        "        _, _, h_old, w_old = img_lq.size()\n",
        "        h_pad = (h_old // window_size + 1) * window_size - h_old\n",
        "        w_pad = (w_old // window_size + 1) * window_size - w_old\n",
        "        img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]\n",
        "        img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]\n",
        "        output = model(img_lq)   # test로 예측한다 -> 살펴보기( ) -> tile이 None일때(기본값) 이렇게 예측\n",
        "        output = output[..., :h_old * scale, :w_old * scale]\n",
        "\n",
        "    # save image\n",
        "    output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()\n",
        "    if output.ndim == 3:\n",
        "        output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))  # CHW-RGB to HCW-BGR\n",
        "    output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8\n",
        "\n",
        "    return output"
      ],
      "metadata": {
        "id": "OEEKAvJQLay9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recognition 에 필요한 모듈 설치 & 정의"
      ],
      "metadata": {
        "id": "_bfx6SrFPt1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "class TPS_SpatialTransformerNetwork(nn.Module):\n",
        "    \"\"\" Rectification Network of RARE, namely TPS based STN \"\"\"\n",
        "\n",
        "    def __init__(self, F, I_size, I_r_size, I_channel_num=1):\n",
        "        \"\"\" Based on RARE TPS\n",
        "        input:\n",
        "            batch_I: Batch Input Image [batch_size x I_channel_num x I_height x I_width]\n",
        "            I_size : (height, width) of the input image I\n",
        "            I_r_size : (height, width) of the rectified image I_r\n",
        "            I_channel_num : the number of channels of the input image I\n",
        "        output:\n",
        "            batch_I_r: rectified image [batch_size x I_channel_num x I_r_height x I_r_width]\n",
        "        \"\"\"\n",
        "        super(TPS_SpatialTransformerNetwork, self).__init__()\n",
        "        self.F = F\n",
        "        self.I_size = I_size\n",
        "        self.I_r_size = I_r_size  # = (I_r_height, I_r_width)\n",
        "        self.I_channel_num = I_channel_num\n",
        "        self.LocalizationNetwork = LocalizationNetwork(self.F, self.I_channel_num)\n",
        "        self.GridGenerator = GridGenerator(self.F, self.I_r_size)\n",
        "\n",
        "    def forward(self, batch_I):\n",
        "        batch_C_prime = self.LocalizationNetwork(batch_I)  # batch_size x K x 2\n",
        "        build_P_prime = self.GridGenerator.build_P_prime(batch_C_prime)  # batch_size x n (= I_r_width x I_r_height) x 2\n",
        "        build_P_prime_reshape = build_P_prime.reshape([build_P_prime.size(0), self.I_r_size[0], self.I_r_size[1], 2])\n",
        "        \n",
        "        if torch.__version__ > \"1.2.0\":\n",
        "            batch_I_r = F.grid_sample(batch_I, build_P_prime_reshape, padding_mode='border', align_corners=True)\n",
        "        else:\n",
        "            batch_I_r = F.grid_sample(batch_I, build_P_prime_reshape, padding_mode='border')\n",
        "\n",
        "        return batch_I_r\n",
        "\n",
        "\n",
        "class LocalizationNetwork(nn.Module):\n",
        "    \"\"\" Localization Network of RARE, which predicts C' (K x 2) from I (I_width x I_height) \"\"\"\n",
        "\n",
        "    def __init__(self, F, I_channel_num):\n",
        "        super(LocalizationNetwork, self).__init__()\n",
        "        self.F = F\n",
        "        self.I_channel_num = I_channel_num\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=self.I_channel_num, out_channels=64, kernel_size=3, stride=1, padding=1,\n",
        "                      bias=False), nn.BatchNorm2d(64), nn.ReLU(True),\n",
        "            nn.MaxPool2d(2, 2),  # batch_size x 64 x I_height/2 x I_width/2\n",
        "            nn.Conv2d(64, 128, 3, 1, 1, bias=False), nn.BatchNorm2d(128), nn.ReLU(True),\n",
        "            nn.MaxPool2d(2, 2),  # batch_size x 128 x I_height/4 x I_width/4\n",
        "            nn.Conv2d(128, 256, 3, 1, 1, bias=False), nn.BatchNorm2d(256), nn.ReLU(True),\n",
        "            nn.MaxPool2d(2, 2),  # batch_size x 256 x I_height/8 x I_width/8\n",
        "            nn.Conv2d(256, 512, 3, 1, 1, bias=False), nn.BatchNorm2d(512), nn.ReLU(True),\n",
        "            nn.AdaptiveAvgPool2d(1)  # batch_size x 512\n",
        "        )\n",
        "\n",
        "        self.localization_fc1 = nn.Sequential(nn.Linear(512, 256), nn.ReLU(True))\n",
        "        self.localization_fc2 = nn.Linear(256, self.F * 2)\n",
        "\n",
        "        # Init fc2 in LocalizationNetwork\n",
        "        self.localization_fc2.weight.data.fill_(0)\n",
        "        \"\"\" see RARE paper Fig. 6 (a) \"\"\"\n",
        "        ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\n",
        "        ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))\n",
        "        ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))\n",
        "        ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\n",
        "        ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\n",
        "        initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\n",
        "        self.localization_fc2.bias.data = torch.from_numpy(initial_bias).float().view(-1)\n",
        "\n",
        "    def forward(self, batch_I):\n",
        "        \"\"\"\n",
        "        input:     batch_I : Batch Input Image [batch_size x I_channel_num x I_height x I_width]\n",
        "        output:    batch_C_prime : Predicted coordinates of fiducial points for input batch [batch_size x F x 2]\n",
        "        \"\"\"\n",
        "        batch_size = batch_I.size(0)\n",
        "        features = self.conv(batch_I).view(batch_size, -1)\n",
        "        batch_C_prime = self.localization_fc2(self.localization_fc1(features)).view(batch_size, self.F, 2)\n",
        "        return batch_C_prime\n",
        "\n",
        "\n",
        "class GridGenerator(nn.Module):\n",
        "    \"\"\" Grid Generator of RARE, which produces P_prime by multipling T with P \"\"\"\n",
        "\n",
        "    def __init__(self, F, I_r_size):\n",
        "        \"\"\" Generate P_hat and inv_delta_C for later \"\"\"\n",
        "        super(GridGenerator, self).__init__()\n",
        "        self.eps = 1e-6\n",
        "        self.I_r_height, self.I_r_width = I_r_size\n",
        "        self.F = F\n",
        "        self.C = self._build_C(self.F)  # F x 2\n",
        "        self.P = self._build_P(self.I_r_width, self.I_r_height)\n",
        "        ## for multi-gpu, you need register buffer\n",
        "        self.register_buffer(\"inv_delta_C\", torch.tensor(self._build_inv_delta_C(self.F, self.C)).float())  # F+3 x F+3\n",
        "        self.register_buffer(\"P_hat\", torch.tensor(self._build_P_hat(self.F, self.C, self.P)).float())  # n x F+3\n",
        "        ## for fine-tuning with different image width, you may use below instead of self.register_buffer\n",
        "        #self.inv_delta_C = torch.tensor(self._build_inv_delta_C(self.F, self.C)).float().cuda()  # F+3 x F+3\n",
        "        #self.P_hat = torch.tensor(self._build_P_hat(self.F, self.C, self.P)).float().cuda()  # n x F+3\n",
        "\n",
        "    def _build_C(self, F):\n",
        "        \"\"\" Return coordinates of fiducial points in I_r; C \"\"\"\n",
        "        ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))\n",
        "        ctrl_pts_y_top = -1 * np.ones(int(F / 2))\n",
        "        ctrl_pts_y_bottom = np.ones(int(F / 2))\n",
        "        ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)\n",
        "        ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)\n",
        "        C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)\n",
        "        return C  # F x 2\n",
        "\n",
        "    def _build_inv_delta_C(self, F, C):\n",
        "        \"\"\" Return inv_delta_C which is needed to calculate T \"\"\"\n",
        "        hat_C = np.zeros((F, F), dtype=float)  # F x F\n",
        "        for i in range(0, F):\n",
        "            for j in range(i, F):\n",
        "                r = np.linalg.norm(C[i] - C[j])\n",
        "                hat_C[i, j] = r\n",
        "                hat_C[j, i] = r\n",
        "        np.fill_diagonal(hat_C, 1)\n",
        "        hat_C = (hat_C ** 2) * np.log(hat_C)\n",
        "        # print(C.shape, hat_C.shape)\n",
        "        delta_C = np.concatenate(  # F+3 x F+3\n",
        "            [\n",
        "                np.concatenate([np.ones((F, 1)), C, hat_C], axis=1),  # F x F+3\n",
        "                np.concatenate([np.zeros((2, 3)), np.transpose(C)], axis=1),  # 2 x F+3\n",
        "                np.concatenate([np.zeros((1, 3)), np.ones((1, F))], axis=1)  # 1 x F+3\n",
        "            ],\n",
        "            axis=0\n",
        "        )\n",
        "        inv_delta_C = np.linalg.inv(delta_C)\n",
        "        return inv_delta_C  # F+3 x F+3\n",
        "\n",
        "    def _build_P(self, I_r_width, I_r_height):\n",
        "        I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + 1.0) / I_r_width  # self.I_r_width\n",
        "        I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + 1.0) / I_r_height  # self.I_r_height\n",
        "        P = np.stack(  # self.I_r_width x self.I_r_height x 2\n",
        "            np.meshgrid(I_r_grid_x, I_r_grid_y),\n",
        "            axis=2\n",
        "        )\n",
        "        return P.reshape([-1, 2])  # n (= self.I_r_width x self.I_r_height) x 2\n",
        "\n",
        "    def _build_P_hat(self, F, C, P):\n",
        "        n = P.shape[0]  # n (= self.I_r_width x self.I_r_height)\n",
        "        P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1))  # n x 2 -> n x 1 x 2 -> n x F x 2\n",
        "        C_tile = np.expand_dims(C, axis=0)  # 1 x F x 2\n",
        "        P_diff = P_tile - C_tile  # n x F x 2\n",
        "        rbf_norm = np.linalg.norm(P_diff, ord=2, axis=2, keepdims=False)  # n x F\n",
        "        rbf = np.multiply(np.square(rbf_norm), np.log(rbf_norm + self.eps))  # n x F\n",
        "        P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1)\n",
        "        return P_hat  # n x F+3\n",
        "\n",
        "    def build_P_prime(self, batch_C_prime):\n",
        "        \"\"\" Generate Grid from batch_C_prime [batch_size x F x 2] \"\"\"\n",
        "        batch_size = batch_C_prime.size(0)\n",
        "        batch_inv_delta_C = self.inv_delta_C.repeat(batch_size, 1, 1)\n",
        "        batch_P_hat = self.P_hat.repeat(batch_size, 1, 1)\n",
        "        batch_C_prime_with_zeros = torch.cat((batch_C_prime, torch.zeros(\n",
        "            batch_size, 3, 2).float().to(device)), dim=1)  # batch_size x F+3 x 2\n",
        "        batch_T = torch.bmm(batch_inv_delta_C, batch_C_prime_with_zeros)  # batch_size x F+3 x 2\n",
        "        batch_P_prime = torch.bmm(batch_P_hat, batch_T)  # batch_size x n x 2\n",
        "        return batch_P_prime  # batch_size x n x 2\n"
      ],
      "metadata": {
        "id": "5nPXXcn5PdXC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = self._conv3x3(inplanes, planes)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = self._conv3x3(planes, planes)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "    def _conv3x3(self, in_planes, out_planes, stride=1):\n",
        "        \"3x3 convolution with padding\"\n",
        "        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n",
        "                         padding=1, bias=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out"
      ],
      "metadata": {
        "id": "_cs_RrDZPgaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResNet(nn.Module):\n",
        "\n",
        "    def __init__(self, input_channel, output_channel, block, layers):\n",
        "        super(ResNet, self).__init__()\n",
        "\n",
        "        self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]\n",
        "\n",
        "        self.inplanes = int(output_channel / 8)\n",
        "        self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),\n",
        "                                 kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))\n",
        "        self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,\n",
        "                                 kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn0_2 = nn.BatchNorm2d(self.inplanes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "        self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])\n",
        "        self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[\n",
        "                               0], kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])\n",
        "\n",
        "        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "        self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)\n",
        "        self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[\n",
        "                               1], kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])\n",
        "\n",
        "        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))\n",
        "        self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)\n",
        "        self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[\n",
        "                               2], kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])\n",
        "\n",
        "        self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)\n",
        "        self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[\n",
        "                                 3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)\n",
        "        self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])\n",
        "        self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[\n",
        "                                 3], kernel_size=2, stride=1, padding=0, bias=False)\n",
        "        self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])\n",
        "\n",
        "    def _make_layer(self, block, planes, blocks, stride=1):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
        "            downsample = nn.Sequential(\n",
        "                nn.Conv2d(self.inplanes, planes * block.expansion,\n",
        "                          kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(planes * block.expansion),\n",
        "            )\n",
        "\n",
        "        layers = []\n",
        "        layers.append(block(self.inplanes, planes, stride, downsample))\n",
        "        self.inplanes = planes * block.expansion\n",
        "        for i in range(1, blocks):\n",
        "            layers.append(block(self.inplanes, planes))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv0_1(x)\n",
        "        x = self.bn0_1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv0_2(x)\n",
        "        x = self.bn0_2(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.maxpool1(x)\n",
        "        x = self.layer1(x)\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.maxpool2(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.bn2(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.maxpool3(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.bn3(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.layer4(x)\n",
        "        x = self.conv4_1(x)\n",
        "        x = self.bn4_1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv4_2(x)\n",
        "        x = self.bn4_2(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "Ri4TawR1PiHt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResNet_FeatureExtractor(nn.Module):\n",
        "    \"\"\" FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) \"\"\"\n",
        "\n",
        "    def __init__(self, input_channel, output_channel=512):\n",
        "        super(ResNet_FeatureExtractor, self).__init__()\n",
        "        self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])\n",
        "\n",
        "    def forward(self, input):\n",
        "        return self.ConvNet(input)"
      ],
      "metadata": {
        "id": "q20vpzu6Pjh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "\n",
        "class CTCLabelConverter(object):\n",
        "    \"\"\" Convert between text-label and text-index \"\"\"\n",
        "\n",
        "    def __init__(self, character):\n",
        "        # character (str): set of the possible characters.\n",
        "        dict_character = list(character)\n",
        "\n",
        "        self.dict = {}\n",
        "        for i, char in enumerate(dict_character):\n",
        "            # NOTE: 0 is reserved for 'CTCblank' token required by CTCLoss\n",
        "            self.dict[char] = i + 1\n",
        "\n",
        "        self.character = ['[CTCblank]'] + dict_character  # dummy '[CTCblank]' token for CTCLoss (index 0)\n",
        "\n",
        "    def encode(self, text, batch_max_length=25):\n",
        "        \"\"\"convert text-label into text-index.\n",
        "        input:\n",
        "            text: text labels of each image. [batch_size]\n",
        "            batch_max_length: max length of text label in the batch. 25 by default\n",
        "\n",
        "        output:\n",
        "            text: text index for CTCLoss. [batch_size, batch_max_length]\n",
        "            length: length of each text. [batch_size]\n",
        "        \"\"\"\n",
        "        length = [len(s) for s in text]\n",
        "\n",
        "        # The index used for padding (=0) would not affect the CTC loss calculation.\n",
        "        batch_text = torch.LongTensor(len(text), batch_max_length).fill_(0)\n",
        "        for i, t in enumerate(text):\n",
        "            text = list(t)\n",
        "            text = [self.dict[char] for char in text]\n",
        "            batch_text[i][:len(text)] = torch.LongTensor(text)\n",
        "        return (batch_text.to(device), torch.IntTensor(length).to(device))\n",
        "\n",
        "    def decode(self, text_index, length):\n",
        "        \"\"\" convert text-index into text-label. \"\"\"\n",
        "        texts = []\n",
        "        for index, l in enumerate(length):\n",
        "            t = text_index[index, :]\n",
        "\n",
        "            char_list = []\n",
        "            for i in range(l):\n",
        "                if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])):  # removing repeated characters and blank.\n",
        "                    char_list.append(self.character[t[i]])\n",
        "            text = ''.join(char_list)\n",
        "\n",
        "            texts.append(text)\n",
        "        return texts"
      ],
      "metadata": {
        "id": "DiCwaG-YPmgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BidirectionalLSTM(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(BidirectionalLSTM, self).__init__()\n",
        "        self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True)\n",
        "        self.linear = nn.Linear(hidden_size * 2, output_size)\n",
        "\n",
        "    def forward(self, input):\n",
        "        \"\"\"\n",
        "        input : visual feature [batch_size x T x input_size]\n",
        "        output : contextual feature [batch_size x T x output_size]\n",
        "        \"\"\"\n",
        "        self.rnn.flatten_parameters()\n",
        "        recurrent, _ = self.rnn(input)  # batch_size x T x input_size -> batch_size x T x (2*hidden_size)\n",
        "        output = self.linear(recurrent)  # batch_size x T x output_size\n",
        "        return output"
      ],
      "metadata": {
        "id": "9gn3SWNwPlFx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from modules.transformation import TPS_SpatialTransformerNetwork\n",
        "# from modules.feature_extraction import VGG_FeatureExtractor, RCNN_FeatureExtractor, ResNet_FeatureExtractor\n",
        "# from modules.sequence_modeling import BidirectionalLSTM\n",
        "# from modules.prediction import Attention\n",
        "\n",
        "\n",
        "class Model(nn.Module):\n",
        "\n",
        "    def __init__(self, Transformation='TPS', FeatureExtraction='ResNet', SequenceModeling='BiLSTM', Prediction='CTC', num_fiducial = 20, imgH = 64, imgW = 200,\n",
        "                 input_channel = 1, output_channel = 512, hidden_size = 256, num_class = 1011):\n",
        "        super(Model, self).__init__()\n",
        "        self.Transformation = Transformation\n",
        "        self.FeatureExtraction = FeatureExtraction\n",
        "        self.SequenceModeling = SequenceModeling\n",
        "        self.Prediction = Prediction\n",
        "\n",
        "        self.num_fiducial = num_fiducial\n",
        "        self.imgH = imgH\n",
        "        self.imgW = imgW\n",
        "        self.input_channel = input_channel\n",
        "        self.output_channel = output_channel\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_class = num_class\n",
        "\n",
        "        self.stages = {'Trans': self.Transformation, 'Feat': self.FeatureExtraction,\n",
        "                       'Seq': self.SequenceModeling, 'Pred': self.Prediction}\n",
        "\n",
        "        \"\"\" Transformation \"\"\"\n",
        "        if self.Transformation == 'TPS':\n",
        "            self.Transformation = TPS_SpatialTransformerNetwork(\n",
        "                F=self.num_fiducial, I_size=(self.imgH, self.imgW), I_r_size=(self.imgH, self.imgW), I_channel_num=self.input_channel)\n",
        "        else:\n",
        "            print('No Transformation module specified')\n",
        "\n",
        "        # Trannsformation으로 TPS 사용\n",
        "\n",
        "        \"\"\" FeatureExtraction \"\"\"\n",
        "        if self.FeatureExtraction == 'VGG':\n",
        "            self.FeatureExtraction = VGG_FeatureExtractor(self.input_channel, self.output_channel)\n",
        "        elif self.FeatureExtraction == 'RCNN':\n",
        "            self.FeatureExtraction = RCNN_FeatureExtractor(self.input_channel, self.output_channel)\n",
        "        elif self.FeatureExtraction == 'ResNet':\n",
        "            self.FeatureExtraction = ResNet_FeatureExtractor(self.input_channel, self.output_channel)\n",
        "        else:\n",
        "            raise Exception('No FeatureExtraction module specified')\n",
        "        self.FeatureExtraction_output = self.output_channel  # int(imgH/16-1) * 512\n",
        "        self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1))  # Transform final (imgH/16-1) -> 1\n",
        "\n",
        "        # FeatureExtraction으로 ResNet 사용, RCNN_FeatureExtractor함수를 사용한다. input_channe과 output_channel이 변수로 들어감\n",
        "\n",
        "        \"\"\" Sequence modeling\"\"\"\n",
        "        if self.SequenceModeling == 'BiLSTM':\n",
        "            self.SequenceModeling = nn.Sequential(\n",
        "                BidirectionalLSTM(self.FeatureExtraction_output, self.hidden_size, self.hidden_size),\n",
        "                BidirectionalLSTM(self.hidden_size, self.hidden_size, self.hidden_size))\n",
        "            self.SequenceModeling_output = self.hidden_size\n",
        "        else:\n",
        "            print('No SequenceModeling module specified')\n",
        "            self.SequenceModeling_output = self.FeatureExtraction_output\n",
        "\n",
        "        \"\"\" Prediction \"\"\"\n",
        "        if self.Prediction == 'CTC':\n",
        "            self.Prediction = nn.Linear(self.SequenceModeling_output, self.num_class)\n",
        "        elif self.Prediction == 'Attn':\n",
        "            self.Prediction = Attention(self.SequenceModeling_output, self.hidden_size, self.num_class)\n",
        "        else:\n",
        "            raise Exception('Prediction is neither CTC or Attn')\n",
        "\n",
        "        \n",
        "        # 여기까지 TPS - ResNet - BiLSTM - CTC 사용에 따른 model을 설정해주는 과정\n",
        "\n",
        "    def forward(self, input, text, is_train=True):\n",
        "        \"\"\" Transformation stage \"\"\"\n",
        "        if not self.stages['Trans'] == \"None\":\n",
        "            input = self.Transformation(input)\n",
        "\n",
        "        \"\"\" Feature extraction stage \"\"\"\n",
        "        visual_feature = self.FeatureExtraction(input)\n",
        "        visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2))  # [b, c, h, w] -> [b, w, c, h]\n",
        "        visual_feature = visual_feature.squeeze(3)\n",
        "\n",
        "        \"\"\" Sequence modeling stage \"\"\"\n",
        "        if self.stages['Seq'] == 'BiLSTM':\n",
        "            contextual_feature = self.SequenceModeling(visual_feature) # 위에서 설정해준 객체\n",
        "        else:\n",
        "            contextual_feature = visual_feature  # for convenience. this is NOT contextually modeled by BiLSTM\n",
        "\n",
        "        \"\"\" Prediction stage \"\"\"\n",
        "        if self.stages['Pred'] == 'CTC':\n",
        "            prediction = self.Prediction(contextual_feature.contiguous()) # 위에서 설정해준 객체\n",
        "        # else:\n",
        "        #     prediction = self.Prediction(contextual_feature.contiguous(), text, is_train, batch_max_length=self.opt.batch_max_length)\n",
        "\n",
        "        return prediction  \n",
        "\n",
        "        # 최종값으로 prediction 리턴해줌\n",
        "        # input으로 image, text로 text_for_pred가 들어감, preds = model(image, text_for_pred)\n",
        "        # text는 CTC 아닌경우에 들어가므로 필요 없을듯?"
      ],
      "metadata": {
        "id": "nk0jXRlRPoet"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms\n",
        "from PIL import Image\n",
        "class ResizeNormalize(object):\n",
        "\n",
        "    def __init__(self, size, interpolation=Image.BICUBIC):\n",
        "        self.size = size\n",
        "        self.interpolation = interpolation\n",
        "        self.toTensor = transforms.ToTensor()\n",
        "\n",
        "    def __call__(self, img):\n",
        "        img = img.resize(self.size, self.interpolation)\n",
        "        img = self.toTensor(img)\n",
        "        img.sub_(0.5).div_(0.5)\n",
        "        return img"
      ],
      "metadata": {
        "id": "S9y2xqIJPPcS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 함수화"
      ],
      "metadata": {
        "id": "MBGrltrzFYX2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "# saved_model = '/content/drive/MyDrive/OCR/TPS-ResNet-BiLSTM-CTC-Seed1111/OrgandAug/100000it/best_norm_ED.pth' # 이거 어떻게 할건지, url, down 이용하는 방법?\n",
        "# character = '0123456789abcdefghijklmnopqrstuvwxyz가각간갇갈감갑값갓강갖같갚갛개객걀걔거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀귓규균귤그극근글긁금급긋긍기긴길김깅깊까깍깎깐깔깜깝깡깥깨꺼꺾껌껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꾼꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냇냉냐냥너넉넌널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐댓더덕던덜덟덤덥덧덩덮데델도독돈돌돕돗동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿링마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몬몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭘뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벨벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브븐블비빌빔빗빚빛빠빡빨빵빼뺏뺨뻐뻔뻗뼈뼉뽑뿌뿐쁘쁨사삭산살삶삼삿상새색샌생샤서석섞선설섬섭섯성세섹센셈셋셔션소속손솔솜솟송솥쇄쇠쇼수숙순숟술숨숫숭숲쉬쉰쉽슈스슨슬슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓴쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액앨야약얀얄얇양얕얗얘어억언얹얻얼엄업없엇엉엊엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷옹와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡잣장잦재쟁쟤저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쩔쩜쪽쫓쭈쭉찌찍찢차착찬찮찰참찻창찾채책챔챙처척천철첩첫청체쳐초촉촌촛총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칫칭카칸칼캄캐캠커컨컬컴컵컷케켓켜코콘콜콤콩쾌쿄쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱턴털텅테텍텔템토톤톨톱통퇴투툴툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔팝패팩팬퍼퍽페펜펴편펼평폐포폭폰표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홈홉홍화확환활황회획횟횡효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘?!'\n",
        "\n",
        "def itt(image, device = torch.device('cuda' if torch.cuda.is_available() else 'cpu'), \n",
        "                saved_model = None, batch_max_length = 25, batch_size = 1, character = character, imgW = 200, imgH = 64):\n",
        "\n",
        "    if saved_model is None:\n",
        "      \n",
        "      url = \"https://drive.google.com/uc?id=1-YU62Q-yIap3yg6u7CCulP58fLS0QdZR\"\n",
        "      output = \"best_norm_ED.pth\"\n",
        "\n",
        "      if not os.path.exists('/content/'+output):\n",
        "        \n",
        "        saved_model = gdown.download(url, output, quiet=False)\n",
        "      saved_model = '/content/'+output\n",
        "      \n",
        "        \n",
        "    else:\n",
        "      saved_model = saved_model\n",
        "\n",
        "    image=Image.fromarray(image)\n",
        "    transform = ResizeNormalize((imgW, imgH)) #imgW, imgH 가 size라는 변수로 들어감\n",
        "    image_tensors = transform(image)\n",
        "    image_tensors = image_tensors.reshape(-1, 1, imgH, imgW)\n",
        "\n",
        "    model = Model()\n",
        "    model = torch.nn.DataParallel(model).to(device)\n",
        "\n",
        "    model.load_state_dict(torch.load(saved_model, map_location=device))\n",
        "    model.eval()\n",
        "\n",
        "    character = '0123456789abcdefghijklmnopqrstuvwxyz가각간갇갈감갑값갓강갖같갚갛개객걀걔거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀귓규균귤그극근글긁금급긋긍기긴길김깅깊까깍깎깐깔깜깝깡깥깨꺼꺾껌껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꾼꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냇냉냐냥너넉넌널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐댓더덕던덜덟덤덥덧덩덮데델도독돈돌돕돗동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿링마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몬몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭘뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벨벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브븐블비빌빔빗빚빛빠빡빨빵빼뺏뺨뻐뻔뻗뼈뼉뽑뿌뿐쁘쁨사삭산살삶삼삿상새색샌생샤서석섞선설섬섭섯성세섹센셈셋셔션소속손솔솜솟송솥쇄쇠쇼수숙순숟술숨숫숭숲쉬쉰쉽슈스슨슬슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓴쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액앨야약얀얄얇양얕얗얘어억언얹얻얼엄업없엇엉엊엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷옹와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡잣장잦재쟁쟤저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쩔쩜쪽쫓쭈쭉찌찍찢차착찬찮찰참찻창찾채책챔챙처척천철첩첫청체쳐초촉촌촛총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칫칭카칸칼캄캐캠커컨컬컴컵컷케켓켜코콘콜콤콩쾌쿄쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱턴털텅테텍텔템토톤톨톱통퇴투툴툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔팝패팩팬퍼퍽페펜펴편펼평폐포폭폰표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홈홉홍화확환활황회획횟횡효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘?!'\n",
        "\n",
        "    converter = CTCLabelConverter(character)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        image = image_tensors.to(device)\n",
        "        text_for_pred = torch.LongTensor(batch_size, batch_max_length + 1).fill_(0).to(device)\n",
        "\n",
        "        preds = model(image, text_for_pred)\n",
        "        preds_size = torch.IntTensor([preds.size(1)] * batch_size)\n",
        "        _, preds_index = preds.max(2)\n",
        "        # preds_index = preds_index.view(-1)\n",
        "        preds_str = converter.decode(preds_index, preds_size)   \n",
        "    \n",
        "    return preds_str"
      ],
      "metadata": {
        "id": "3-sW7_ugOwpk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Detection-SR-Recognition"
      ],
      "metadata": {
        "id": "tGK3HyZh8g9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import torch\n",
        "from PIL import Image\n",
        "\n",
        "def yolov5s_detect(img, save_path = None) :\n",
        "    # !gdown --id 10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\n",
        "    # path = '/content/best.pt'\n",
        "    # model = torch.hub.load('ultralytics/yolov5', 'custom', path=path)\n",
        "\n",
        "    if save_path is None:\n",
        "      \n",
        "      url = \"https://drive.google.com/uc?id=10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\"\n",
        "      output = \"best.pt\"\n",
        "\n",
        "      if not os.path.exists('/content/'+output):\n",
        "        \n",
        "        save_path = gdown.download(url, output, quiet=False)\n",
        "      save_path = '/content/'+output\n",
        "      \n",
        "    else:\n",
        "      save_path = save_path\n",
        "\n",
        "    model = torch.hub.load('ultralytics/yolov5', 'custom', path=save_path)\n",
        "\n",
        "\n",
        "    data_img = img\n",
        "\n",
        "    results = model(data_img)\n",
        "    print('탐지된 이미지의 수 : ',len(results.xyxy[0]))\n",
        "    \n",
        "    crop_images = []\n",
        "    for idx in range(len(results.xyxy[0])) :\n",
        "\n",
        "        xy = results.xyxy[0][idx].cpu().numpy()\n",
        "\n",
        "        start = (int(xy[1]),int(xy[0])) # y, x좌표 min값\n",
        "        end = (int(xy[3]),int(xy[2])) # y, x좌표 max값\n",
        "\n",
        "        output = np.zeros((end[0]-start[0], end[1]-start[1], 3), np.uint8)\n",
        "        # print(output.shape)\n",
        "\n",
        "        for y in range(output.shape[1]):\n",
        "            for x in range(output.shape[0]):\n",
        "                xp, yp = x + start[0], y+start[1]\n",
        "                output[x,y] = img[xp,yp]\n",
        "\n",
        "        # crop image\n",
        "        crop_image = np.asarray(output)\n",
        " \n",
        "        crop_images.append(crop_image)\n",
        "  \n",
        "        \n",
        "    return crop_images, results.xyxy[0]"
      ],
      "metadata": {
        "id": "jkIZ-1-38iTH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 이미지 위에 text 쓰기 \n",
        "- 원본 이미지의 bbox좌표에 text 넣기"
      ],
      "metadata": {
        "id": "wjhSzDxvyJRB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# model_path = '/content/drive/MyDrive/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth'\n",
        "# saved_model = '/content/drive/MyDrive/OCR/TPS-ResNet-BiLSTM-CTC-Seed1111/OrgandAug/100000it/best_norm_ED.pth' # 이거 어떻게 할건지, url, down 이용하는 방법?\n",
        "# character = '0123456789abcdefghijklmnopqrstuvwxyz가각간갇갈감갑값갓강갖같갚갛개객걀걔거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀귓규균귤그극근글긁금급긋긍기긴길김깅깊까깍깎깐깔깜깝깡깥깨꺼꺾껌껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꾼꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냇냉냐냥너넉넌널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐댓더덕던덜덟덤덥덧덩덮데델도독돈돌돕돗동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿링마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몬몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭘뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벨벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브븐블비빌빔빗빚빛빠빡빨빵빼뺏뺨뻐뻔뻗뼈뼉뽑뿌뿐쁘쁨사삭산살삶삼삿상새색샌생샤서석섞선설섬섭섯성세섹센셈셋셔션소속손솔솜솟송솥쇄쇠쇼수숙순숟술숨숫숭숲쉬쉰쉽슈스슨슬슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓴쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액앨야약얀얄얇양얕얗얘어억언얹얻얼엄업없엇엉엊엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷옹와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡잣장잦재쟁쟤저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쩔쩜쪽쫓쭈쭉찌찍찢차착찬찮찰참찻창찾채책챔챙처척천철첩첫청체쳐초촉촌촛총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칫칭카칸칼캄캐캠커컨컬컴컵컷케켓켜코콘콜콤콩쾌쿄쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱턴털텅테텍텔템토톤톨톱통퇴투툴툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔팝패팩팬퍼퍽페펜펴편펼평폐포폭폰표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홈홉홍화확환활황회획횟횡효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘?!'\n",
        "\n",
        "for file_name in os.listdir('/content/test'):\n",
        "    img = cv2.imread('/content/test/'+file_name)\n",
        "    crop_images, bboxs = yolov5s_detect(img)\n",
        "\n",
        "    texts = []\n",
        "    for crop_image in crop_images:\n",
        "        sr_img = sr(crop_image)\n",
        "        text = itt(sr_img)\n",
        "        texts.append(text)\n",
        "\n",
        "    print(texts)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DEJ4Gj6877F",
        "outputId": "dad71bf6-2cc9-481b-ea6b-6c95478d1dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\n",
            "To: /content/best.pt\n",
            "100% 14.8M/14.8M [00:00<00:00, 173MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (6.0)\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /root/.cache/torch/hub/ultralytics_yolov5_master/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "YOLOv5 🚀 2022-7-1 Python-3.7.13 torch-1.11.0+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients\n",
            "Adding AutoShape... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "탐지된 이미지의 수 :  2\n",
            "tensor([[159.99484,  44.88322, 198.75583,  60.17443,   0.76253,   0.00000],\n",
            "        [113.58392,  44.31783, 150.74527,  60.11415,   0.74708,   0.00000]], device='cuda:0')\n",
            "[['사진관'], ['여기에']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image, ImageDraw,ImageFilter, ImageFont\n",
        "import cv2\n",
        "import gdown\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\"\"\"\n",
        "tensor([[159.99484,  44.88322, 198.75583,  60.17443,   0.76253,   0.00000],\n",
        "        [113.58392,  44.31783, 150.74527,  60.11415,   0.74708,   0.00000]], device='cuda:0')\"\"\"\n",
        "\n",
        "\n",
        "def img_blur_text(image, bboxs, texts, mag=30, font_down = None):\n",
        "  \n",
        "    if font_down is None:\n",
        "      \n",
        "      url = \"https://drive.google.com/uc?id=17IK1YuODQxjJDQtVEqOF22EtvDh37wUs\"\n",
        "      output = \"best.pt\"\n",
        "\n",
        "      if not os.path.exists('/content/'+output):\n",
        "        \n",
        "        font_down = gdown.download(url, output, quiet=False)\n",
        "      font_down = '/content/'+output\n",
        "      \n",
        "    else:\n",
        "      font_down = font_down\n",
        "\n",
        "\n",
        "    #   img = Image.open(image).convert('RGB')\n",
        "    #   blurI = img.filter(ImageFilter.GaussianBlur(50))\n",
        "\n",
        "    \n",
        "    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "  \n",
        "    \n",
        "    for bbox, text in zip(bboxs, texts):\n",
        "        x = int(bbox[0])\n",
        "        y = int(bbox[1])\n",
        "        x_ = int(bbox[2])\n",
        "        y_ = int(bbox[3])\n",
        "\n",
        "        roi = img[y:y_, x:x_]\n",
        "        roi = cv2.blur(roi, (mag, mag))\n",
        "        img[y:y_, x:x_] = roi\n",
        "\n",
        "        img_p = Image.fromarray(img)\n",
        "\n",
        "        text_size = int((bbox[3]-bbox[1])*0.9)\n",
        "\n",
        "        # 글자 위치\n",
        "        xs, ys = int(x*1.02), int(y*1.02)\n",
        "        text_pos = (xs, ys)\n",
        "\n",
        "        # 글자 타입 ( 글자 폰트, 글자 크기)\n",
        "        font_type = ImageFont.truetype('/content/NanumBarunGothic.ttf',text_size)\n",
        "    \n",
        "        # 글자색\n",
        "        color = (0,0,0)\n",
        "\n",
        "        draw = ImageDraw.Draw(img_p)\n",
        "\n",
        "        draw.text(text_pos, text[0], color, font=font_type)\n",
        "        \n",
        "        img = np.array(img_p) \n",
        "        # 텍스트가 쓰여진 이미지를 다시 배열로 바꿔서 for문을 돌 수 있게 사용\n",
        "\n",
        "    return img\n",
        "\n",
        "    \n",
        "\n",
        "  \n"
      ],
      "metadata": {
        "id": "Nv0p9b5mKDJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "테스트"
      ],
      "metadata": {
        "id": "CErLPu4Bbmrs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = '/content/test/1.jpg'\n",
        "texts = [['사진관'], ['여기에']]\n",
        "bboxs = [[159.99484,  44.88322, 198.75583,  60.17443,   0.76253,   0.00000],\n",
        "         [113.58392,  44.31783, 150.74527,  60.11415,   0.74708,   0.00000]]\n",
        "image = cv2.imread(img_path)\n",
        "img = img_blur_text(image=image, bboxs=bboxs, texts=texts)\n",
        "plt.imshow(img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "3tz9m4wbMD7a",
        "outputId": "7c06947d-a047-43ba-fc1f-7a121ae3774b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17IK1YuODQxjJDQtVEqOF22EtvDh37wUs\n",
            "To: /content/NanumBarunGothic.ttf\n",
            "100%|██████████| 4.19M/4.19M [00:00<00:00, 71.8MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff5a1f419d0>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Detection - SR - Recognition - drawtext"
      ],
      "metadata": {
        "id": "VownwJEsathw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "model_path = '/content/drive/MyDrive/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth'\n",
        "saved_model = '/content/drive/MyDrive/OCR/TPS-ResNet-BiLSTM-CTC-Seed1111/OrgandAug/100000it/best_norm_ED.pth' # 이거 어떻게 할건지, url, down 이용하는 방법?\n",
        "character = '0123456789abcdefghijklmnopqrstuvwxyz가각간갇갈감갑값갓강갖같갚갛개객걀걔거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀귓규균귤그극근글긁금급긋긍기긴길김깅깊까깍깎깐깔깜깝깡깥깨꺼꺾껌껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꾼꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냇냉냐냥너넉넌널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐댓더덕던덜덟덤덥덧덩덮데델도독돈돌돕돗동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿링마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몬몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭘뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벨벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브븐블비빌빔빗빚빛빠빡빨빵빼뺏뺨뻐뻔뻗뼈뼉뽑뿌뿐쁘쁨사삭산살삶삼삿상새색샌생샤서석섞선설섬섭섯성세섹센셈셋셔션소속손솔솜솟송솥쇄쇠쇼수숙순숟술숨숫숭숲쉬쉰쉽슈스슨슬슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓴쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액앨야약얀얄얇양얕얗얘어억언얹얻얼엄업없엇엉엊엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷옹와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡잣장잦재쟁쟤저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쩔쩜쪽쫓쭈쭉찌찍찢차착찬찮찰참찻창찾채책챔챙처척천철첩첫청체쳐초촉촌촛총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칫칭카칸칼캄캐캠커컨컬컴컵컷케켓켜코콘콜콤콩쾌쿄쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱턴털텅테텍텔템토톤톨톱통퇴투툴툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔팝패팩팬퍼퍽페펜펴편펼평폐포폭폰표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홈홉홍화확환활황회획횟횡효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘?!'\n",
        "\n",
        "for file_name in os.listdir('/content/test'):\n",
        "    img = cv2.imread('/content/test/'+file_name)\n",
        "    crop_images, bboxs = yolov5s_detect(img)\n",
        "\n",
        "    texts = []\n",
        "    for crop_image in crop_images:\n",
        "        sr_img = sr(crop_image)\n",
        "        text = itt(sr_img)\n",
        "        texts.append(text)\n",
        "    \n",
        "    img_t = img_blur_text(image=img, bboxs=bboxs, texts=texts)\n",
        "\n",
        "    print(texts)\n",
        "    plt.imshow(img_t)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "FLiZdmB4MaDX",
        "outputId": "bb812633-e502-4a76-d860-69ceaeef3bd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\n",
            "To: /content/best.pt\n",
            "100% 14.8M/14.8M [00:00<00:00, 218MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "YOLOv5 🚀 2022-7-1 Python-3.7.13 torch-1.11.0+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients\n",
            "Adding AutoShape... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "탐지된 이미지의 수 :  2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17IK1YuODQxjJDQtVEqOF22EtvDh37wUs\n",
            "To: /content/NanumBarunGothic.ttf\n",
            "100%|██████████| 4.19M/4.19M [00:00<00:00, 197MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['사진관'], ['여기에']]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = '/content/test/5.jpg'\n",
        "img = cv2.imread(img_path)\n",
        "crop_images, bboxs = yolov5s_detect(img)\n",
        "\n",
        "texts = []\n",
        "for crop_image in crop_images:\n",
        "    sr_img = sr(crop_image)\n",
        "    text = itt(sr_img)\n",
        "    texts.append(text)\n",
        "\n",
        "img_t = img_blur_text(image=img, bboxs=bboxs, texts=texts)\n",
        "\n",
        "print(texts)\n",
        "plt.imshow(img_t)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "qtU_YP1kakpq",
        "outputId": "0ee710c6-dc0c-421d-8bbc-55743fafcd58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\n",
            "To: /content/best.pt\n",
            "100% 14.8M/14.8M [00:00<00:00, 316MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "YOLOv5 🚀 2022-7-1 Python-3.7.13 torch-1.11.0+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients\n",
            "Adding AutoShape... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "탐지된 이미지의 수 :  2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17IK1YuODQxjJDQtVEqOF22EtvDh37wUs\n",
            "To: /content/NanumBarunGothic.ttf\n",
            "100%|██████████| 4.19M/4.19M [00:00<00:00, 232MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['꽃과나무'], ['글te']]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff5a3009990>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = '/content/test/1.jpg'\n",
        "img = cv2.imread(img_path)\n",
        "crop_images, bboxs = yolov5s_detect(img)\n",
        "\n",
        "texts = []\n",
        "for crop_image in crop_images:\n",
        "    sr_img = sr(crop_image)\n",
        "    text = itt(sr_img)\n",
        "    texts.append(text)\n",
        "\n",
        "img_t = img_blur_text(image=img, bboxs=bboxs, texts=texts)\n",
        "\n",
        "print(texts)\n",
        "plt.imshow(img_t)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "r-_5WYGycVQF",
        "outputId": "199ea42e-c3c0-403c-df85-2ce992168c95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=10xmrzFfeRjVUWGS9onsuDkLrrRylrhLw\n",
            "To: /content/best.pt\n",
            "100% 14.8M/14.8M [00:00<00:00, 212MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "YOLOv5 🚀 2022-7-1 Python-3.7.13 torch-1.11.0+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients\n",
            "Adding AutoShape... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "탐지된 이미지의 수 :  2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17IK1YuODQxjJDQtVEqOF22EtvDh37wUs\n",
            "To: /content/NanumBarunGothic.ttf\n",
            "100%|██████████| 4.19M/4.19M [00:00<00:00, 210MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['사진관'], ['여기에']]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff5a158b610>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "v53nKlaFdL9G"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}