{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f7f852c",
   "metadata": {},
   "source": [
    "# Plant Disease Classification Runtime Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b53d416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from email.mime import image\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import timm\n",
    "import torch\n",
    "import torchvision.transforms.v2 as T\n",
    "from sklearn.metrics import (\n",
    "    average_precision_score,\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    ")\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from torch import nn\n",
    "from torch.cuda.amp import autocast\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from src.dataset import PlantDiseaseDataset\n",
    "from src.metrics import MetricTracker\n",
    "from src.models import PlantDiseaseModel\n",
    "from src.utils import load_config, set_seed\n",
    "\n",
    "plt.style.use(\"seaborn-v0_8-muted\")\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "DATA_DIR = \"data\"\n",
    "TRAIN_DIR = os.path.join(DATA_DIR, \"train\")\n",
    "VAL_DIR = os.path.join(DATA_DIR, \"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b410943",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "label_map_path = os.path.join(DATA_DIR, \"label_map.json\")\n",
    "with open(label_map_path) as f:\n",
    "    label_map = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1ff7421",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size = 384\n",
    "\n",
    "transforms = T.Compose(\n",
    "    [\n",
    "        T.Resize((image_size, image_size), antialias=True),\n",
    "        T.ToImage(),\n",
    "        T.ToDtype(torch.float32, scale=True),\n",
    "        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "val_dataset = PlantDiseaseDataset(\n",
    "    VAL_DIR,\n",
    "    label_map=label_map,\n",
    "    transform=transforms,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f07362ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "pin_memory = False\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    num_workers=4,\n",
    "    pin_memory=pin_memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5d39689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backbone_name = \"convnextv2_tiny.fcmae_ft_in22k_in1k_384\"\n",
    "checkpoint_path = \"checkpoints/cn_best.pt\"\n",
    "mixed_precision = False\n",
    "drop_path = 0.2\n",
    "drop_rate = 0.2\n",
    "\n",
    "\n",
    "class PlantDiseaseModel(nn.Module):\n",
    "    def __init__(self, config, num_classes):\n",
    "        super().__init__()\n",
    "        self.backbone_name = config\n",
    "\n",
    "        self.model = timm.create_model(\n",
    "            self.backbone_name,\n",
    "            pretrained=True,\n",
    "            num_classes=num_classes,\n",
    "            drop_rate=drop_rate,\n",
    "            drop_path_rate=drop_path,\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "\n",
    "model = PlantDiseaseModel(backbone_name, num_classes=39)\n",
    "checkpoint = torch.load(checkpoint_path, map_location=device)\n",
    "state_dict = checkpoint.get(\"state_dict\", checkpoint)\n",
    "model.load_state_dict(state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "387eb431",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "scripted_model = torch.jit.script(model)\n",
    "scripted_model.save(\"convnext_scripted.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "589e1184",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.jit.load(\"checkpoints/convnext_scripted.pt\").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16f81694",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.infer import get_tta_transforms\n",
    "from sklearn.metrics import accuracy_score, average_precision_score\n",
    "\n",
    "\n",
    "def evaluate(model, val_loader, device=None, use_tta=False, image_size=384):\n",
    "    if device is None:\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    model = model.to(device)\n",
    "    model.eval()\n",
    "\n",
    "    if use_tta:\n",
    "        tta_transforms = get_tta_transforms(image_size)\n",
    "\n",
    "    all_probs = []\n",
    "    all_labels = []\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        for images, labels in val_loader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            if use_tta:\n",
    "                tta_batches = []\n",
    "\n",
    "                for transform in tta_transforms:\n",
    "                    augmented = torch.stack([transform(img.cpu()) for img in images])\n",
    "                    tta_batches.append(augmented)\n",
    "\n",
    "                tta_batches = torch.stack(tta_batches).to(device)\n",
    "\n",
    "                outputs = []\n",
    "                for tta_batch in tta_batches:\n",
    "                    out = model(tta_batch)  # [batch, num_classes]\n",
    "                    outputs.append(out)\n",
    "\n",
    "                outputs = torch.stack(outputs).mean(dim=0)\n",
    "\n",
    "            else:\n",
    "                outputs = model(images)\n",
    "\n",
    "            probs = torch.softmax(outputs, dim=1)\n",
    "\n",
    "            all_probs.append(probs.cpu())\n",
    "            all_labels.append(labels.cpu())\n",
    "\n",
    "    all_probs = torch.cat(all_probs).numpy()\n",
    "    all_labels = torch.cat(all_labels).numpy()\n",
    "\n",
    "    preds = np.argmax(all_probs, axis=1)\n",
    "    acc = accuracy_score(all_labels, preds)\n",
    "\n",
    "    num_classes = all_probs.shape[1]\n",
    "    y_true_bin = np.zeros((len(all_labels), num_classes))\n",
    "    y_true_bin[np.arange(len(all_labels)), all_labels] = 1\n",
    "\n",
    "    per_class_ap = []\n",
    "    for i in range(num_classes):\n",
    "        if y_true_bin[:, i].sum() > 0:\n",
    "            ap = average_precision_score(y_true_bin[:, i], all_probs[:, i])\n",
    "            per_class_ap.append(ap)\n",
    "\n",
    "    mAP = np.mean(per_class_ap)\n",
    "\n",
    "    return acc, mAP, all_probs, all_labels\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(preds, all_labels, label_map):\n",
    "    idx_to_disease = {v: k for k, v in label_map.items()}\n",
    "    cm = confusion_matrix(all_labels, preds)\n",
    "    disease_names = [idx_to_disease[i] for i in range(len(label_map))]\n",
    "\n",
    "    plt.figure(figsize=(20, 16))\n",
    "    sns.heatmap(\n",
    "        cm,\n",
    "        annot=True,\n",
    "        fmt=\"d\",\n",
    "        cmap=\"Blues\",\n",
    "        xticklabels=disease_names,\n",
    "        yticklabels=disease_names,\n",
    "    )\n",
    "    plt.xlabel(\"Predicted\")\n",
    "    plt.ylabel(\"True\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.xticks(rotation=90, fontsize=7)\n",
    "    plt.yticks(fontsize=7)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be009977",
   "metadata": {},
   "outputs": [],
   "source": [
    "effnet = torch.jit.load(\"effnet_scripted.pt\").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82cef07",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc, mAP, all_probs, all_labels = evaluate(effnet, val_loader, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aef53b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(all_probs, all_labels, label_map)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}