DNN_SwinTransformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fT-k2qfVS3We",
    "outputId": "859cdb61-d3e8-4c1a-f89f-3e7393559936"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchinfo in /usr/local/lib/python3.11/dist-packages (1.8.0)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "!pip install torchinfo\n",
    "from torchinfo import summary\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "import random\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9iw5KqP_JU6r",
    "outputId": "990ee3a2-40d0-4e5a-94f1-a36290efa78e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Using device:\", device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xreO-nBwpwZi"
   },
   "source": [
    "# Fonction d'aide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "ajm_0u60pzbs"
   },
   "outputs": [],
   "source": [
    "def window_partition(x, window_size):\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "TpO9bcyqp4c2"
   },
   "outputs": [],
   "source": [
    "def window_reverse(windows, window_size, H, W):\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JQse6bXA5apP"
   },
   "source": [
    "# Patch Partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Kmr_Dm_tVLSU"
   },
   "outputs": [],
   "source": [
    "class PatchPartition(nn.Module):\n",
    "  # On passe d'une image WxHx3 à W/4xH/4x96\n",
    "    def __init__(self, in_channels=3, patch_size=4, embed_dim=96):\n",
    "        \"\"\"\n",
    "        in_channels : 3 pcq RGB\n",
    "        patch_size: size  patch (4 pcq /4).\n",
    "        embed_dim: dimension embedding (C = 96)\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.patch_size = patch_size\n",
    "        self.embed_dim = embed_dim\n",
    "        self.projection = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        (B, C, H, W)\n",
    "        B = batch_size, C = channels, H = height, W = width\n",
    "        Et va return\n",
    "        (B, H/patch_size * W/patch_size, embed_dim)\n",
    "        \"\"\"\n",
    "        x = self.projection(x)\n",
    "        x = x.permute(0, 2, 3, 1)\n",
    "        B, Hp, Wp, C = x.shape\n",
    "        x = x.view(B, Hp * Wp, C)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TpDNl2ki5pDL"
   },
   "source": [
    "# Linear Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kDQonSgyXceW"
   },
   "outputs": [],
   "source": [
    "class LinearEmbedding(nn.Module):\n",
    "    def __init__(self, in_features, embed_dim):\n",
    "        \"\"\"\n",
    "        in_features: dismension de l'input\n",
    "        embed_dim: dimension output (target) donc (C)\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.linear_proj = nn.Linear(in_features, embed_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        (B, H', W', C)\n",
    "        Et va return\n",
    "        (B, H', W', embed_dim)\n",
    "        embed dim : \"A linear embedding layer is applied on this raw-valued feature to project it to an arbitrary dimension (denoted as C )\"\n",
    "        On doit choisir C. Test avec 96 ?\n",
    "        \"\"\"\n",
    "        B, H, W, C = x.shape\n",
    "        x = x.view(B, -1, C)  #(B, H' * W', in_features)\n",
    "        x = self.linear_proj(x)  #(B, H' * W', embed_dim)\n",
    "        x = x.view(B, H, W, -1)  #(B, H', W', embed_dim)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "68i5OPm16xAj"
   },
   "source": [
    "# PatchMerging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "DqXuu-m3bb9s"
   },
   "outputs": [],
   "source": [
    "class PatchMerging(nn.Module):\n",
    "    def __init__(self, in_channels, dim):\n",
    "        super().__init__()\n",
    "        self.originale_dim = in_channels\n",
    "        self.dim =dim\n",
    "        self.LayerNorm = nn.LayerNorm( 4 * dim)  # Normalistation pour 4 dimentions\n",
    "        self.reduction = nn.Linear(4*dim, 2*dim, bias=False) # Réduction de C\n",
    "\n",
    "    def forward(self, x):\n",
    "      Height,Width = self.originale_dim\n",
    "      B, L, C = x.shape\n",
    "      x = x.view(B, Height, Width, C)\n",
    "\n",
    "      # On prends les 4 blocks En haut à gauche, bas gauche, haut droite, bas droite\n",
    "      # Prendre une ligne/colonne sur 2 permet de faire une réduction spaciale\n",
    "      B1 = x[:, 0::2, 0::2,:]\n",
    "      B2 = x[:, 1::2, 0::2,:]\n",
    "      B3 = x[:, 0::2, 1::2,:]\n",
    "      B4 = x[:, 1::2, 1::2,:]\n",
    "\n",
    "      x = torch.cat([B1, B2, B3, B4], dim=-1)  # On merge pour avoir la sortie qu'on veut\n",
    "      x = x.view(B, -1, 4 * C)  #Reshape en (B, H/4, W/4, 4*C) Avec B = Batch Size, H = Height, W = Width, C = facteur inconnue\n",
    "      # -1 => Laisse Pytorch trouver lui même la taille du tenseur\n",
    "\n",
    "      x = self.LayerNorm(x) # Normalisation sur les 4*C\n",
    "      x = self.reduction(x) # Réduction pour aller de 4*C à 2 *C\n",
    "\n",
    "      return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xUNVl8HbZhxd"
   },
   "source": [
    "# Swin Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m3F9QVT1e3Es"
   },
   "source": [
    "### MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Cpg6DCkSe4GV"
   },
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "  def __init__(self, in_features, hidden_features, out_features, drop):\n",
    "    super().__init__()\n",
    "    out_features = out_features\n",
    "    hidden_features = hidden_features\n",
    "    in_features = in_features\n",
    "    drop = drop\n",
    "    self.fc1 = nn.Linear(in_features, hidden_features)\n",
    "    self.gelu = nn.GELU()\n",
    "    self.fc2 = nn.Linear(hidden_features, out_features)\n",
    "    #Dropout non dit dans le papier de recherche\n",
    "    self.dropout = nn.Dropout(drop)\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.fc1(x)\n",
    "    x = self.gelu(x)\n",
    "    x = self.dropout(x)\n",
    "    x = self.fc2(x)\n",
    "    x = self.dropout(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "05gJmF7ZaUWh"
   },
   "source": [
    "### Window-based Multi-Head Self-Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "CZpNQPa88R3F"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class WMSA(nn.Module):\n",
    "    def __init__(self, dim, window_size, num_heads):\n",
    "        #On a a faire le \"relative position bias\"\n",
    "        #Et le mask si on a shifted windows\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        self.window_size = window_size\n",
    "        self.num_heads = num_heads\n",
    "        self.scale = (dim // num_heads) ** -0.5\n",
    "\n",
    "        #init l'attnetion Q K V\n",
    "        self.qkv = nn.Linear(dim, dim * 3, bias=True)\n",
    "        self.proj = nn.Linear(dim, dim)\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "        # Relative position index + bias table\n",
    "        #On calcul le (dx, dy) (position relative) d'un patch pour tout les patchs\n",
    "        coords_h = torch.arange(window_size[0])\n",
    "        coords_w = torch.arange(window_size[1])\n",
    "        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))\n",
    "        coords_flatten = coords.flatten(1)\n",
    "        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] #On a donc (2, wh*ww, wh*ww)\n",
    "        relative_coords = relative_coords.permute(1, 2, 0).contiguous() #ici (wh*ww, wh*ww, 2)\n",
    "        relative_coords[:, :, 0] += window_size[0] - 1\n",
    "        relative_coords[:, :, 1] += window_size[1] - 1\n",
    "        relative_coords[:, :, 0] *= (2 * window_size[1] - 1)\n",
    "        relative_position_index = relative_coords.sum(-1)\n",
    "        self.register_buffer(\"relative_position_index\", relative_position_index)\n",
    "        self.relative_position_bias_table = nn.Parameter(torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))\n",
    "\n",
    "    def forward(self, x, mask=None):\n",
    "        B_, N, C = x.shape\n",
    "        # 1) qkv\n",
    "        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n",
    "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
    "        q, k, v = qkv[0], qkv[1], qkv[2]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        q = q * self.scale\n",
    "        attn = q @ k.transpose(-2, -1)\n",
    "\n",
    "        # Ajoute le relative bias\n",
    "        bias_table = self.relative_position_bias_table\n",
    "        index = self.relative_position_index.view(-1)\n",
    "        relative_position_bias = bias_table[index].view(N, N, self.num_heads)\n",
    "        relative_position_bias = relative_position_bias.permute(2, 0, 1)\n",
    "        attn = attn + relative_position_bias.unsqueeze(0)\n",
    "\n",
    "        #On fait le mask, donc on enleve les liens lors du shift avec les immage d'arrivé à coté\n",
    "        if mask is not None:\n",
    "            nW = mask.shape[0]\n",
    "            attn = attn.view(B_ // nW, nW, self.num_heads, N, N)\n",
    "            attn = attn + mask.unsqueeze(1).unsqueeze(0)\n",
    "            attn = attn.view(-1, self.num_heads, N, N)\n",
    "\n",
    "        attn = self.softmax(attn)\n",
    "        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n",
    "        x = self.proj(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZijIUbUqCk9f"
   },
   "source": [
    "## Swin Transformer Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "GvTj4KI58dcN"
   },
   "outputs": [],
   "source": [
    "class SwinTransformerBlock(nn.Module):\n",
    "    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., 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",
    "\n",
    "\n",
    "        #La premier normalisation\n",
    "        self.norm1 = norm_layer(dim)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        #Le window multi head self attention on l'initialise\n",
    "        self.attn = WMSA(dim, window_size=(window_size, window_size), num_heads=num_heads)\n",
    "        #Un drop path ?\n",
    "        self.drop_path = nn.Identity()\n",
    "\n",
    "        #La deuxieme normalisa  tion\n",
    "        self.norm2 = norm_layer(dim)\n",
    "\n",
    "\n",
    "\n",
    "        #Le MLP\n",
    "        mlp_hidden_dim = int(dim * mlp_ratio)\n",
    "        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, drop=0.1)\n",
    "\n",
    "\n",
    "        #Pré calcule du mash d'attentino\n",
    "        if self.shift_size > 0:\n",
    "            self.attn_mask = self.create_attn_mask(self.input_resolution)\n",
    "        else:\n",
    "            self.attn_mask = None\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    def create_attn_mask(self, input_resolution):\n",
    "        H, W = input_resolution\n",
    "        mask = torch.zeros((1, H, W, 1), dtype=torch.int32)\n",
    "        h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))\n",
    "        w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))\n",
    "        cnt = 0\n",
    "        for h in h_slices:\n",
    "            for w in w_slices:\n",
    "                mask[:, h, w, :] = cnt\n",
    "                cnt += 1\n",
    "\n",
    "\n",
    "        mask_windows = window_partition(mask, self.window_size)\n",
    "        mask_windows = mask_windows.view(-1, self.window_size**2)\n",
    "\n",
    "        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n",
    "        #Zone different ou non\n",
    "        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0))\n",
    "        attn_mask = attn_mask.masked_fill(attn_mask == 0, float(0.0))\n",
    "\n",
    "        self.register_buffer(\"attn_mask\", attn_mask, persistent=False)\n",
    "        return attn_mask\n",
    "\n",
    "    def forward(self, x):\n",
    "        B, L, C = x.shape\n",
    "        H, W = self.input_resolution\n",
    "        assert L == H * W, \"Wrong size\"\n",
    "\n",
    "        #Normalisation\n",
    "        shortcut = x\n",
    "        x = self.norm1(x).view(B, H, W, C)\n",
    "\n",
    "\n",
    "\n",
    "        #Le shift window\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",
    "\n",
    "        #Decoupage des fenetres\n",
    "        x_windows = window_partition(shifted_x, self.window_size)\n",
    "        x_windows = x_windows.view(-1, self.window_size**2, C)\n",
    "\n",
    "        #attentino\n",
    "        attn_windows = self.attn(x_windows, mask=self.attn_mask)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\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)\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",
    "\n",
    "        x = x.view(B, H*W, C)\n",
    "        x = shortcut + self.drop_path(x)\n",
    "\n",
    "\n",
    "\n",
    "        #MLP\n",
    "        shortcut = x\n",
    "        x = self.norm2(x)\n",
    "        x = self.mlp(x)\n",
    "        x = shortcut + self.drop_path(x)\n",
    "\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DqTScw3Y86My"
   },
   "source": [
    "## Basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "uZDqPr8t9ImB"
   },
   "outputs": [],
   "source": [
    "class BlockLayer(nn.Module):\n",
    "    def __init__(self, dim, input_resolution, depth, num_heads, window_size=7, mlp_ratio=4., use_shift=True, downsample=None, norm_layer=nn.LayerNorm,):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        self.input_resolution = input_resolution\n",
    "        self.depth = depth\n",
    "        self.window_size = window_size\n",
    "        self.use_shift = use_shift\n",
    "\n",
    "\n",
    "        self.blocks = nn.ModuleList()\n",
    "        for i in range(depth):\n",
    "            # calcul shift\n",
    "            shift = window_size // 2 if (use_shift and i % 2 == 1) else 0\n",
    "            block = SwinTransformerBlock(dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=shift, mlp_ratio=mlp_ratio, norm_layer=norm_layer)\n",
    "            self.blocks.append(block)\n",
    "\n",
    "        #PatchMerging\n",
    "        if downsample is not None:\n",
    "            self.downsample = downsample(input_resolution, dim=dim)\n",
    "        else:\n",
    "            self.downsample = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        for blk in self.blocks:\n",
    "            x = blk(x)\n",
    "        if self.downsample is not None:\n",
    "            x = self.downsample(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQpke75h9MPF"
   },
   "source": [
    "## Swin Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "qWIlUB629emb"
   },
   "outputs": [],
   "source": [
    "class SwinTransformer(nn.Module):\n",
    "    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4., use_shift=True,):\n",
    "        super().__init__()\n",
    "        self.num_classes = num_classes\n",
    "        self.num_layers = len(depths)\n",
    "        self.embed_dim = embed_dim\n",
    "\n",
    "        #Patch Partition au debut\n",
    "        self.patch_embed = PatchPartition(in_channels=in_chans, patch_size=patch_size, embed_dim=embed_dim)\n",
    "        H_ = img_size // patch_size\n",
    "        W_ = img_size // patch_size\n",
    "        self.patches_resolution = (H_, W_)\n",
    "        self.pos_drop = nn.Dropout(p=0.0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        #On fait les stages en fonction du depth\n",
    "        self.layers = nn.ModuleList()\n",
    "        dim_current = embed_dim\n",
    "        H, W = self.patches_resolution\n",
    "        for i_layer in range(self.num_layers):\n",
    "            stage = BlockLayer(dim=dim_current, input_resolution=(H, W), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=mlp_ratio, use_shift=use_shift, downsample=PatchMerging if (i_layer < self.num_layers - 1) else None)\n",
    "            self.layers.append(stage)\n",
    "            if i_layer < self.num_layers - 1:\n",
    "                H, W = H // 2, W // 2\n",
    "                dim_current *= 2\n",
    "        self.norm = nn.LayerNorm(dim_current)\n",
    "        self.head = nn.Linear(dim_current, num_classes) if num_classes > 0 else nn.Identity()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.patch_embed(x)\n",
    "        x = self.pos_drop(x)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "        x = self.norm(x)\n",
    "        x = x.mean(dim=1)\n",
    "        x = self.head(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuaWne-a9ckL"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JR-_NtAEGLfe",
    "outputId": "7631039d-b645-4d62-8b18-c8f06fa2a726"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/torch/functional.py:534: 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:3595.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "====================================================================================================\n",
       "Layer (type:depth-idx)                             Output Shape              Param #\n",
       "====================================================================================================\n",
       "SwinTransformer                                    [1, 1000]                 --\n",
       "├─PatchPartition: 1-1                              [1, 3136, 96]             --\n",
       "│    └─Conv2d: 2-1                                 [1, 96, 56, 56]           4,704\n",
       "├─Dropout: 1-2                                     [1, 3136, 96]             --\n",
       "├─ModuleList: 1-3                                  --                        --\n",
       "│    └─BlockLayer: 2-2                             [1, 784, 192]             --\n",
       "│    │    └─ModuleList: 3-1                        --                        224,694\n",
       "│    │    └─PatchMerging: 3-2                      [1, 784, 192]             74,496\n",
       "│    └─BlockLayer: 2-3                             [1, 196, 384]             --\n",
       "│    │    └─ModuleList: 3-3                        --                        891,756\n",
       "│    │    └─PatchMerging: 3-4                      [1, 196, 384]             296,448\n",
       "│    └─BlockLayer: 2-4                             [1, 49, 768]              --\n",
       "│    │    └─ModuleList: 3-5                        --                        10,658,952\n",
       "│    │    └─PatchMerging: 3-6                      [1, 49, 768]              1,182,720\n",
       "│    └─BlockLayer: 2-5                             [1, 49, 768]              --\n",
       "│    │    └─ModuleList: 3-7                        --                        14,183,856\n",
       "├─LayerNorm: 1-4                                   [1, 49, 768]              1,536\n",
       "├─Linear: 1-5                                      [1, 1000]                 769,000\n",
       "====================================================================================================\n",
       "Total params: 28,288,162\n",
       "Trainable params: 28,288,162\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 62.80\n",
       "====================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 134.88\n",
       "Params size (MB): 113.06\n",
       "Estimated Total Size (MB): 248.54\n",
       "===================================================================================================="
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SwinTransformer(img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2,2,6,2], num_heads=[3,6,12,24], window_size=7, mlp_ratio=4., use_shift=True,)\n",
    "\n",
    "model = model.to(device)\n",
    "\n",
    "summary(model, input_size=(1, 3, 224, 224), device=str(device))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e4O8O8CM2NsP"
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rKqiG-eJ2fIj",
    "outputId": "67af6139-ff60-4497-a4a9-e25ab71fecb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in /usr/local/lib/python3.11/dist-packages (1.6.17)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.11/dist-packages (from kaggle) (1.17.0)\n",
      "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2024.12.14)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.32.3)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from kaggle) (4.67.1)\n",
      "Requirement already satisfied: python-slugify in /usr/local/lib/python3.11/dist-packages (from kaggle) (8.0.4)\n",
      "Requirement already satisfied: urllib3 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.3.0)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.11/dist-packages (from kaggle) (6.2.0)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from bleach->kaggle) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.11/dist-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->kaggle) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->kaggle) (3.10)\n"
     ]
    }
   ],
   "source": [
    "!pip install kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On va download un subdataset de ImageNet1K. Ici, nous avons 100 classes contenant 1300 images chacune**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2-wCXBe_CNrw",
    "outputId": "5622b083-ce5a-4c8a-da49-194bae68071c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path to dataset files: /root/.cache/kagglehub/datasets/ambityga/imagenet100/versions/8\n"
     ]
    }
   ],
   "source": [
    "import kagglehub\n",
    "\n",
    "path = kagglehub.dataset_download(\"ambityga/imagenet100\")\n",
    "print(\"Path to dataset files:\", path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yzBbDfSRFvUU",
    "outputId": "0f50ec43-56ee-4f2a-e356-05d3941d4a91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of train images: 130000\n",
      "Number of val images: 5000\n"
     ]
    }
   ],
   "source": [
    "ROOT_DATASET = \"/root/.cache/kagglehub/datasets/ambityga/imagenet100/versions/8\"\n",
    "\n",
    "with open(os.path.join(ROOT_DATASET, \"Labels.json\"), \"r\") as f:\n",
    "    labels_map = json.load(f)\n",
    "\n",
    "train_image_paths = []\n",
    "train_labels = []\n",
    "train_folders = [\"train.X1\", \"train.X2\", \"train.X3\", \"train.X4\"]\n",
    "for folder_name in train_folders:\n",
    "    folder_path = os.path.join(ROOT_DATASET, folder_name)\n",
    "    for class_dir in os.listdir(folder_path):\n",
    "        class_folder = os.path.join(folder_path, class_dir)\n",
    "        if not os.path.isdir(class_folder):\n",
    "            continue\n",
    "        for img_name in os.listdir(class_folder):\n",
    "            if img_name.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
    "                img_path = os.path.join(class_folder, img_name)\n",
    "                train_image_paths.append(img_path)\n",
    "                train_labels.append(class_dir)\n",
    "\n",
    "\n",
    "val_image_paths = []\n",
    "val_labels = []\n",
    "val_folder = os.path.join(ROOT_DATASET, \"val.X\")\n",
    "for class_dir in os.listdir(val_folder):\n",
    "    class_folder = os.path.join(val_folder, class_dir)\n",
    "    if not os.path.isdir(class_folder):\n",
    "        continue\n",
    "    for img_name in os.listdir(class_folder):\n",
    "        if img_name.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
    "            img_path = os.path.join(class_folder, img_name)\n",
    "            val_image_paths.append(img_path)\n",
    "            val_labels.append(class_dir)\n",
    "print(f\"Number of train images: {len(train_image_paths)}\")\n",
    "print(f\"Number of val images: {len(val_image_paths)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a1paAARQGERZ",
    "outputId": "92a5fd9c-ebee-41d9-da5f-f85c2bb5e044"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final val set size: 4500\n",
      "Final test set size: 500\n"
     ]
    }
   ],
   "source": [
    "val_data = list(zip(val_image_paths, val_labels))\n",
    "random.shuffle(val_data)\n",
    "\n",
    "n_val = len(val_data)\n",
    "n_test = int(0.1 * n_val)\n",
    "test_data = val_data[:n_test]\n",
    "val_data = val_data[n_test:]\n",
    "\n",
    "val_image_paths, val_labels = zip(*val_data)\n",
    "test_image_paths, test_labels = zip(*test_data)\n",
    "\n",
    "print(\"Final val set size:\", len(val_image_paths))\n",
    "print(\"Final test set size:\", len(test_image_paths))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "SSTb2j99GZz1"
   },
   "outputs": [],
   "source": [
    "class ImageNetLikeDataset(Dataset):\n",
    "    def __init__(self, image_paths, labels, class_to_idx, transform=None):\n",
    "        self.image_paths = image_paths\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "        self.class_to_idx = class_to_idx\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_paths)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img_path = self.image_paths[index]\n",
    "        label_name = self.labels[index]\n",
    "        label_idx = self.class_to_idx[label_name]\n",
    "\n",
    "        image = Image.open(img_path).convert(\"RGB\")\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return image, label_idx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "GIiDHh9IGdAj"
   },
   "outputs": [],
   "source": [
    "all_classes = sorted(set(train_labels + list(val_labels) + list(test_labels)))\n",
    "class_to_idx = {cname: i for i, cname in enumerate(all_classes)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3tGDP1KU5aiP",
    "outputId": "992ee672-9b24-4f05-941f-0bacc76919f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 130000\n",
      "Val dataset size: 4500\n",
      "Test dataset size: 500\n"
     ]
    }
   ],
   "source": [
    "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n",
    "IMAGENET_STD = (0.229, 0.224, 0.225)\n",
    "\n",
    "transform_train = transforms.Compose([transforms.Resize((224, 224)), transforms.RandomHorizontalFlip(), transforms.ColorJitter(0.4, 0.4, 0.4, 0.1), transforms.ToTensor(), transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),])\n",
    "transform_eval = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),])\n",
    "transform_test = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),])\n",
    "\n",
    "train_dataset = ImageNetLikeDataset(image_paths=train_image_paths, labels=train_labels, class_to_idx=class_to_idx, transform=transform_train)\n",
    "val_dataset = ImageNetLikeDataset(image_paths=val_image_paths, labels=val_labels, class_to_idx=class_to_idx, transform=transform_eval)\n",
    "test_dataset = ImageNetLikeDataset(image_paths=test_image_paths, labels=test_labels, class_to_idx=class_to_idx, transform=transform_eval)\n",
    "\n",
    "print(\"Train dataset size:\", len(train_dataset))\n",
    "print(\"Val dataset size:\", len(val_dataset))\n",
    "print(\"Test dataset size:\", len(test_dataset))\n",
    "\n",
    "num_classes = len(class_to_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "W2rLaFtO51pk"
   },
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "num_workers = 4\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3u-6M-KI583c",
    "outputId": "18a885a9-57e6-4bf6-b104-eca1de4ca727"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 130000\n",
      "Val dataset size: 4500\n",
      "Test dataset size: 500\n",
      "Number of classes: 100\n",
      "Train Class Distribution (class_index -> count):\n",
      "Class 9: 1300\n",
      "Class 0: 1300\n",
      "Class 75: 1300\n",
      "Class 29: 1300\n",
      "Class 20: 1300\n",
      "Class 59: 1300\n",
      "Class 4: 1300\n",
      "Class 90: 1300\n",
      "Class 53: 1300\n",
      "Class 73: 1300\n",
      "Class 31: 1300\n",
      "Class 2: 1300\n",
      "Class 55: 1300\n",
      "Class 82: 1300\n",
      "Class 62: 1300\n",
      "Class 23: 1300\n",
      "Class 88: 1300\n",
      "Class 83: 1300\n",
      "Class 93: 1300\n",
      "Class 65: 1300\n",
      "Class 74: 1300\n",
      "Class 41: 1300\n",
      "Class 78: 1300\n",
      "Class 61: 1300\n",
      "Class 96: 1300\n",
      "Class 32: 1300\n",
      "Class 66: 1300\n",
      "Class 33: 1300\n",
      "Class 43: 1300\n",
      "Class 51: 1300\n",
      "Class 48: 1300\n",
      "Class 49: 1300\n",
      "Class 50: 1300\n",
      "Class 7: 1300\n",
      "Class 39: 1300\n",
      "Class 99: 1300\n",
      "Class 27: 1300\n",
      "Class 79: 1300\n",
      "Class 92: 1300\n",
      "Class 46: 1300\n",
      "Class 98: 1300\n",
      "Class 10: 1300\n",
      "Class 8: 1300\n",
      "Class 95: 1300\n",
      "Class 37: 1300\n",
      "Class 94: 1300\n",
      "Class 35: 1300\n",
      "Class 15: 1300\n",
      "Class 13: 1300\n",
      "Class 1: 1300\n",
      "Class 80: 1300\n",
      "Class 87: 1300\n",
      "Class 89: 1300\n",
      "Class 30: 1300\n",
      "Class 63: 1300\n",
      "Class 77: 1300\n",
      "Class 36: 1300\n",
      "Class 45: 1300\n",
      "Class 16: 1300\n",
      "Class 21: 1300\n",
      "Class 67: 1300\n",
      "Class 11: 1300\n",
      "Class 64: 1300\n",
      "Class 24: 1300\n",
      "Class 17: 1300\n",
      "Class 91: 1300\n",
      "Class 71: 1300\n",
      "Class 19: 1300\n",
      "Class 6: 1300\n",
      "Class 14: 1300\n",
      "Class 28: 1300\n",
      "Class 22: 1300\n",
      "Class 86: 1300\n",
      "Class 12: 1300\n",
      "Class 42: 1300\n",
      "Class 47: 1300\n",
      "Class 97: 1300\n",
      "Class 34: 1300\n",
      "Class 57: 1300\n",
      "Class 68: 1300\n",
      "Class 72: 1300\n",
      "Class 54: 1300\n",
      "Class 3: 1300\n",
      "Class 44: 1300\n",
      "Class 81: 1300\n",
      "Class 84: 1300\n",
      "Class 60: 1300\n",
      "Class 69: 1300\n",
      "Class 38: 1300\n",
      "Class 76: 1300\n",
      "Class 5: 1300\n",
      "Class 26: 1300\n",
      "Class 18: 1300\n",
      "Class 25: 1300\n",
      "Class 70: 1300\n",
      "Class 85: 1300\n",
      "Class 40: 1300\n",
      "Class 56: 1300\n",
      "Class 52: 1300\n",
      "Class 58: 1300\n"
     ]
    }
   ],
   "source": [
    "print(\"Train dataset size:\", len(train_dataset))\n",
    "print(\"Val dataset size:\", len(val_dataset))\n",
    "print(\"Test dataset size:\", len(test_dataset))\n",
    "print(\"Number of classes:\", len(train_dataset.class_to_idx))\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "train_targets = [train_dataset.class_to_idx[label_name] for label_name in train_dataset.labels]\n",
    "class_counts = Counter(train_targets)\n",
    "print(\"Train Class Distribution (class_index -> count):\")\n",
    "for cls_idx, cnt in class_counts.most_common():\n",
    "    print(f\"Class {cls_idx}: {cnt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 260
    },
    "id": "sKDaWlYk6DRX",
    "outputId": "3a361277-41c7-4156-f25c-108a2993ed16"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = next(iter(train_loader))\n",
    "images = images[:4]\n",
    "labels = labels[:4]\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def unnorm(img):\n",
    "    mean = np.array(IMAGENET_MEAN)\n",
    "    std = np.array(IMAGENET_STD)\n",
    "    img = img.cpu().numpy().transpose(1, 2, 0)\n",
    "    img = (img * std + mean).clip(0, 1)\n",
    "    return img\n",
    "fig, axs = plt.subplots(1, 4, figsize=(12, 3))\n",
    "for i in range(4):\n",
    "    axs[i].imshow(unnorm(images[i]))\n",
    "    label_index = labels[i].item()\n",
    "    if label_index in train_dataset.class_to_idx:\n",
    "        axs[i].set_title(train_dataset.class_to_idx[label_index])\n",
    "    else:\n",
    "        axs[i].set_title(f\"Unknown label: {label_index}\")\n",
    "    axs[i].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nNCNVbIs6-bq",
    "outputId": "29309693-42f2-4786-8176-8b7cc761bfa1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Using device:\", device)\n",
    "\n",
    "model = SwinTransformer(img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2,2,6,2], num_heads=[3,6,12,24], window_size=7, mlp_ratio=4., use_shift=True,).to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GcO_4XcYKMXB",
    "outputId": "90fca1fc-5941-4617-ed3a-97508b71ed0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "QUWJbd6Y8N4U"
   },
   "outputs": [],
   "source": [
    "optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=0.05)\n",
    "\n",
    "num_epochs = 75\n",
    "warmup_epochs = 5\n",
    "\n",
    "def lr_schedule(epoch):\n",
    "    if epoch < warmup_epochs:\n",
    "        return float(epoch) / float(warmup_epochs)\n",
    "    else:\n",
    "        progress = float(epoch - warmup_epochs) / float(num_epochs - warmup_epochs)\n",
    "        return 0.5 * (1.0 + np.cos(np.pi * progress))\n",
    "\n",
    "\n",
    "scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EIZ7dXAG8g_p",
    "outputId": "1252b685-0142-4d7a-87de-a30e8e459a32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/75] Train Loss: 7.0539, Train Acc: 0.11% | Val Loss: 7.0637, Val Acc: 0.09%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [2/75] Train Loss: 4.7374, Train Acc: 6.39% | Val Loss: 4.2029, Val Acc: 15.51%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [3/75] Train Loss: 3.9055, Train Acc: 21.02% | Val Loss: 3.4600, Val Acc: 30.80%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [4/75] Train Loss: 3.3572, Train Acc: 33.33% | Val Loss: 3.1380, Val Acc: 39.38%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [5/75] Train Loss: 3.0602, Train Acc: 40.67% | Val Loss: 2.9632, Val Acc: 43.11%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [6/75] Train Loss: 2.8736, Train Acc: 45.41% | Val Loss: 2.8727, Val Acc: 46.47%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [7/75] Train Loss: 2.6794, Train Acc: 50.54% | Val Loss: 2.7202, Val Acc: 50.20%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [8/75] Train Loss: 2.5354, Train Acc: 54.39% | Val Loss: 2.6476, Val Acc: 52.02%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [9/75] Train Loss: 2.4134, Train Acc: 57.84% | Val Loss: 2.5880, Val Acc: 54.07%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [10/75] Train Loss: 2.3090, Train Acc: 60.57% | Val Loss: 2.4927, Val Acc: 56.24%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [11/75] Train Loss: 2.2195, Train Acc: 63.06% | Val Loss: 2.5072, Val Acc: 55.89%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [12/75] Train Loss: 2.1322, Train Acc: 65.52% | Val Loss: 2.4080, Val Acc: 59.11%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [13/75] Train Loss: 2.0480, Train Acc: 67.87% | Val Loss: 2.3735, Val Acc: 60.33%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [14/75] Train Loss: 1.9722, Train Acc: 70.02% | Val Loss: 2.3673, Val Acc: 60.29%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [15/75] Train Loss: 1.8969, Train Acc: 72.26% | Val Loss: 2.3366, Val Acc: 62.04%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [16/75] Train Loss: 1.8179, Train Acc: 74.85% | Val Loss: 2.3122, Val Acc: 62.24%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [17/75] Train Loss: 1.7488, Train Acc: 76.73% | Val Loss: 2.3454, Val Acc: 61.96%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [18/75] Train Loss: 1.6830, Train Acc: 78.80% | Val Loss: 2.3557, Val Acc: 61.76%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [19/75] Train Loss: 1.6093, Train Acc: 81.36% | Val Loss: 2.3491, Val Acc: 62.49%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [20/75] Train Loss: 1.5448, Train Acc: 83.45% | Val Loss: 2.3401, Val Acc: 63.67%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [21/75] Train Loss: 1.4800, Train Acc: 85.71% | Val Loss: 2.3750, Val Acc: 64.02%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [22/75] Train Loss: 1.4311, Train Acc: 87.45% | Val Loss: 2.3931, Val Acc: 63.62%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [23/75] Train Loss: 1.3822, Train Acc: 89.22% | Val Loss: 2.4254, Val Acc: 62.44%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [24/75] Train Loss: 1.3381, Train Acc: 90.95% | Val Loss: 2.4631, Val Acc: 61.91%\n",
      "  [*] No improvement for 3 epochs.\n",
      "Epoch [25/75] Train Loss: 1.3019, Train Acc: 92.37% | Val Loss: 2.4587, Val Acc: 63.53%\n",
      "  [*] No improvement for 4 epochs.\n",
      "Epoch [26/75] Train Loss: 1.2695, Train Acc: 93.52% | Val Loss: 2.4785, Val Acc: 62.82%\n",
      "  [*] No improvement for 5 epochs.\n",
      "Epoch [27/75] Train Loss: 1.2478, Train Acc: 94.25% | Val Loss: 2.4470, Val Acc: 63.53%\n",
      "  [*] No improvement for 6 epochs.\n",
      "Epoch [28/75] Train Loss: 1.2259, Train Acc: 95.10% | Val Loss: 2.4782, Val Acc: 64.29%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [29/75] Train Loss: 1.2100, Train Acc: 95.64% | Val Loss: 2.4992, Val Acc: 63.49%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [30/75] Train Loss: 1.1955, Train Acc: 96.08% | Val Loss: 2.4604, Val Acc: 64.20%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [31/75] Train Loss: 1.1808, Train Acc: 96.58% | Val Loss: 2.4984, Val Acc: 63.71%\n",
      "  [*] No improvement for 3 epochs.\n",
      "Epoch [32/75] Train Loss: 1.1703, Train Acc: 96.90% | Val Loss: 2.4758, Val Acc: 63.69%\n",
      "  [*] No improvement for 4 epochs.\n",
      "Epoch [33/75] Train Loss: 1.1567, Train Acc: 97.36% | Val Loss: 2.4676, Val Acc: 64.49%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [34/75] Train Loss: 1.1507, Train Acc: 97.46% | Val Loss: 2.4874, Val Acc: 64.29%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [35/75] Train Loss: 1.1378, Train Acc: 97.82% | Val Loss: 2.4284, Val Acc: 65.09%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [36/75] Train Loss: 1.1304, Train Acc: 97.99% | Val Loss: 2.4965, Val Acc: 64.58%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [37/75] Train Loss: 1.1219, Train Acc: 98.19% | Val Loss: 2.4411, Val Acc: 65.16%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [38/75] Train Loss: 1.1171, Train Acc: 98.27% | Val Loss: 2.4697, Val Acc: 64.33%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [39/75] Train Loss: 1.1098, Train Acc: 98.43% | Val Loss: 2.4254, Val Acc: 65.47%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [40/75] Train Loss: 1.0999, Train Acc: 98.66% | Val Loss: 2.4739, Val Acc: 65.36%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [41/75] Train Loss: 1.0933, Train Acc: 98.80% | Val Loss: 2.4492, Val Acc: 65.98%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [42/75] Train Loss: 1.0889, Train Acc: 98.84% | Val Loss: 2.4186, Val Acc: 65.80%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [43/75] Train Loss: 1.0854, Train Acc: 98.88% | Val Loss: 2.4589, Val Acc: 65.91%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [44/75] Train Loss: 1.0789, Train Acc: 98.95% | Val Loss: 2.4389, Val Acc: 65.62%\n",
      "  [*] No improvement for 3 epochs.\n",
      "Epoch [45/75] Train Loss: 1.0746, Train Acc: 99.03% | Val Loss: 2.4355, Val Acc: 65.80%\n",
      "  [*] No improvement for 4 epochs.\n",
      "Epoch [46/75] Train Loss: 1.0725, Train Acc: 99.01% | Val Loss: 2.4260, Val Acc: 66.71%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [47/75] Train Loss: 1.0657, Train Acc: 99.12% | Val Loss: 2.4256, Val Acc: 66.44%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [48/75] Train Loss: 1.0631, Train Acc: 99.12% | Val Loss: 2.4573, Val Acc: 66.42%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [49/75] Train Loss: 1.0585, Train Acc: 99.17% | Val Loss: 2.4394, Val Acc: 67.18%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [50/75] Train Loss: 1.0556, Train Acc: 99.20% | Val Loss: 2.4490, Val Acc: 66.51%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [51/75] Train Loss: 1.0544, Train Acc: 99.18% | Val Loss: 2.4504, Val Acc: 67.00%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [52/75] Train Loss: 1.0507, Train Acc: 99.24% | Val Loss: 2.4486, Val Acc: 67.24%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [53/75] Train Loss: 1.0471, Train Acc: 99.28% | Val Loss: 2.4269, Val Acc: 67.38%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [54/75] Train Loss: 1.0454, Train Acc: 99.26% | Val Loss: 2.4304, Val Acc: 67.80%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [55/75] Train Loss: 1.0427, Train Acc: 99.25% | Val Loss: 2.4333, Val Acc: 67.93%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [56/75] Train Loss: 1.0414, Train Acc: 99.24% | Val Loss: 2.4405, Val Acc: 67.69%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [57/75] Train Loss: 1.0397, Train Acc: 99.29% | Val Loss: 2.4376, Val Acc: 67.62%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [58/75] Train Loss: 1.0384, Train Acc: 99.28% | Val Loss: 2.4433, Val Acc: 68.09%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [59/75] Train Loss: 1.0367, Train Acc: 99.30% | Val Loss: 2.4467, Val Acc: 67.96%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [60/75] Train Loss: 1.0353, Train Acc: 99.31% | Val Loss: 2.4591, Val Acc: 67.96%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [61/75] Train Loss: 1.0343, Train Acc: 99.30% | Val Loss: 2.4655, Val Acc: 68.16%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [62/75] Train Loss: 1.0333, Train Acc: 99.33% | Val Loss: 2.4525, Val Acc: 68.93%\n",
      "  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\n",
      "Epoch [63/75] Train Loss: 1.0325, Train Acc: 99.32% | Val Loss: 2.4763, Val Acc: 68.33%\n",
      "  [*] No improvement for 1 epochs.\n",
      "Epoch [64/75] Train Loss: 1.0316, Train Acc: 99.32% | Val Loss: 2.4646, Val Acc: 68.71%\n",
      "  [*] No improvement for 2 epochs.\n",
      "Epoch [65/75] Train Loss: 1.0307, Train Acc: 99.36% | Val Loss: 2.4750, Val Acc: 68.49%\n",
      "  [*] No improvement for 3 epochs.\n",
      "Epoch [66/75] Train Loss: 1.0301, Train Acc: 99.34% | Val Loss: 2.4721, Val Acc: 68.56%\n",
      "  [*] No improvement for 4 epochs.\n",
      "Epoch [67/75] Train Loss: 1.0297, Train Acc: 99.36% | Val Loss: 2.4856, Val Acc: 68.33%\n",
      "  [*] No improvement for 5 epochs.\n",
      "Epoch [68/75] Train Loss: 1.0291, Train Acc: 99.38% | Val Loss: 2.4836, Val Acc: 68.60%\n",
      "  [*] No improvement for 6 epochs.\n",
      "Epoch [69/75] Train Loss: 1.0287, Train Acc: 99.39% | Val Loss: 2.4889, Val Acc: 68.93%\n",
      "  [*] No improvement for 7 epochs.\n",
      "Epoch [70/75] Train Loss: 1.0283, Train Acc: 99.40% | Val Loss: 2.4912, Val Acc: 68.78%\n",
      "  [*] No improvement for 8 epochs.\n",
      "Epoch [71/75] Train Loss: 1.0282, Train Acc: 99.40% | Val Loss: 2.4909, Val Acc: 68.73%\n",
      "  [*] No improvement for 9 epochs.\n",
      "Epoch [72/75] Train Loss: 1.0279, Train Acc: 99.44% | Val Loss: 2.4915, Val Acc: 68.91%\n",
      "  [*] No improvement for 10 epochs.\n",
      "Early stopping triggered. Best Val Acc: 68.93%\n"
     ]
    }
   ],
   "source": [
    "criterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "\n",
    "best_val_acc = 0.0\n",
    "\n",
    "patience = 10\n",
    "epochs_no_improve = 0\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    #Training\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for images, labels in train_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = outputs.max(dim=1)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        total += labels.size(0)\n",
    "\n",
    "    train_loss = running_loss / len(train_loader)\n",
    "    train_acc = 100.0 * correct / total\n",
    "    scheduler.step()\n",
    "\n",
    "    #Validation\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    with torch.no_grad():\n",
    "        for images, labels in val_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            val_loss += loss.item()\n",
    "\n",
    "            _, predicted = outputs.max(dim=1)\n",
    "            val_correct += (predicted == labels).sum().item()\n",
    "            val_total += labels.size(0)\n",
    "\n",
    "    val_loss /= len(val_loader)\n",
    "    val_acc = 100.0 * val_correct / val_total\n",
    "\n",
    "    print(f\"Epoch [{epoch+1}/{num_epochs}] \"\n",
    "          f\"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}% | \"\n",
    "          f\"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%\")\n",
    "\n",
    "    if val_acc > best_val_acc:\n",
    "        best_val_acc = val_acc\n",
    "        checkpoint_path = \"/content/drive/MyDrive/swin_best.pth\"\n",
    "        torch.save(model.state_dict(), checkpoint_path)\n",
    "        print(f\"  [*] Best model saved to: /content/drive/MyDrive/swin_best.pth\")\n",
    "        epochs_no_improve = 0\n",
    "    else:\n",
    "        epochs_no_improve += 1\n",
    "        print(f\"  [*] No improvement for {epochs_no_improve} epochs.\")\n",
    "\n",
    "\n",
    "    if epochs_no_improve >= patience:\n",
    "        print(f\"Early stopping triggered. Best Val Acc: {best_val_acc:.2f}%\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Cv4zm8e9Dm-g",
    "outputId": "64be94a4-b7cc-458d-9cce-a6af0b2ad383"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-97674e276fe8>:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(best_model_path))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 69.20% on 500 images.\n"
     ]
    }
   ],
   "source": [
    "best_model_path = \"/content/drive/MyDrive/swin_best.pth\"\n",
    "model.load_state_dict(torch.load(best_model_path))\n",
    "model.eval()\n",
    "\n",
    "test_correct = 0\n",
    "test_total = 0\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, pred = outputs.max(dim=1)\n",
    "        test_correct += (pred == labels).sum().item()\n",
    "        test_total += labels.size(0)\n",
    "\n",
    "test_acc = 100.0 * test_correct / test_total\n",
    "print(f\"Test Accuracy: {test_acc:.2f}% on {test_total} images.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3OWACCtlUVUj"
   },
   "source": [
    "**Nous avons une accuracy de 70% de la donnée de test**"
   ]
  }
 ],
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    "6ItCA7aQ9EMX",
    "19jaQgxG9SUU",
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   "gpuType": "A100",
   "machine_shape": "hm",
   "provenance": []
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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