{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3813f900-ea54-4533-873a-70aa46417ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ SpA-GAN Data Ready: 29117 samples\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# CELL 1: SETUP & LOAD DỮ LIỆU\n",
    "# ==========================================\n",
    "import os, sys, torch, numpy as np\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "# Fix Import Path\n",
    "sys.path.append(os.getcwd())\n",
    "sys.path.append(\"/home/jovyan/cloud_train\")\n",
    "\n",
    "try:\n",
    "    from sen12ms_cr_dataLoader import SEN12MSCRDataset, Seasons, S1Bands, S2Bands\n",
    "except ImportError:\n",
    "    raise RuntimeError(\"❌ Không tìm thấy file 'sen12ms_cr_dataLoader.py'\")\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "base_dir = os.path.join(os.path.expanduser(\"~\"), \"cloud_train\", \"dataset\")\n",
    "\n",
    "class SEN12MSCR_TorchDataset(Dataset):\n",
    "    def __init__(self, base_dir, season=Seasons.SPRING):\n",
    "        self.loader = SEN12MSCRDataset(base_dir)\n",
    "        self.season = season\n",
    "        self.season_ids = self.loader.get_season_ids(season)\n",
    "        self.samples = [(sid, pid) for sid, pids in self.season_ids.items() for pid in pids]\n",
    "    def __len__(self): return len(self.samples)\n",
    "    def __getitem__(self, idx):\n",
    "        sid, pid = self.samples[idx]\n",
    "        s1, s2, c, _ = self.loader.get_s1s2s2cloudy_triplet(self.season, sid, pid)\n",
    "        s1 = np.clip(s1, -25, 0) / 25.0\n",
    "        s2 = (np.clip(s2, 0, 10000) / 5000.0) - 1.0\n",
    "        c = (np.clip(c, 0, 10000) / 5000.0) - 1.0\n",
    "        return torch.from_numpy(s1).float(), torch.from_numpy(s2).float(), torch.from_numpy(c).float()\n",
    "\n",
    "train_dataset = SEN12MSCR_TorchDataset(base_dir)\n",
    "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=0)\n",
    "print(f\"✅ SpA-GAN Data Ready: {len(train_dataset)} samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "078b6951-1dcf-44e9-895c-9ec0c8c848b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# CELL 2: MODEL SpA-GAN\n",
    "# ==========================================\n",
    "class SpatialAttention(nn.Module):\n",
    "    def __init__(self, in_channels):\n",
    "        super(SpatialAttention, self).__init__()\n",
    "        self.conv = nn.Conv2d(in_channels, 1, kernel_size=1) \n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "    def forward(self, x):\n",
    "        return x * self.sigmoid(self.conv(x))\n",
    "\n",
    "class SpA_Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SpA_Generator, self).__init__()\n",
    "        self.enc1 = nn.Sequential(nn.Conv2d(15, 64, 4, 2, 1), nn.LeakyReLU(0.2))\n",
    "        self.attn = SpatialAttention(64) # Module Attention\n",
    "        self.enc2 = nn.Sequential(nn.Conv2d(64, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2))\n",
    "        \n",
    "        self.dec1 = nn.Sequential(nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.ReLU())\n",
    "        self.dec2 = nn.ConvTranspose2d(64, 13, 4, 2, 1)\n",
    "        self.tanh = nn.Tanh()\n",
    "\n",
    "    def forward(self, s1, s2_cloudy):\n",
    "        x = torch.cat([s1, s2_cloudy], dim=1)\n",
    "        e1 = self.enc1(x)\n",
    "        e1_attn = self.attn(e1) # Chú ý vào vùng quan trọng\n",
    "        e2 = self.enc2(e1_attn)\n",
    "        d1 = self.dec1(e2)\n",
    "        return self.tanh(self.dec2(d1 + e1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6bd89be-aa61-4429-b588-f2041311f2a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Bắt đầu train SpA-GAN...\n",
      "Step 0 | Loss G: 0.6843\n",
      "Step 10 | Loss G: 0.3739\n",
      "Step 20 | Loss G: 0.1896\n",
      "Step 30 | Loss G: 0.1224\n",
      "Step 40 | Loss G: 0.0938\n",
      "Step 50 | Loss G: 0.1833\n",
      "\n",
      "💾 Đã lưu model tại: /home/jovyan/cloud_train/saved_models/SpAGAN_generator.pth\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# CELL 3: TRAINING & SAVE\n",
    "# ==========================================\n",
    "if 'train_loader' in locals():\n",
    "    gen = SpA_Generator().to(device)\n",
    "    opt_g = torch.optim.Adam(gen.parameters(), lr=0.0002)\n",
    "    criterion = nn.L1Loss()\n",
    "    \n",
    "    print(\"🚀 Bắt đầu train SpA-GAN...\")\n",
    "    for i, (s1, s2, cloud) in enumerate(train_loader):\n",
    "        s1, s2, cloud = s1.to(device), s2.to(device), cloud.to(device)\n",
    "        \n",
    "        opt_g.zero_grad()\n",
    "        fake = gen(s1, cloud)\n",
    "        loss_g = criterion(fake, s2) \n",
    "        loss_g.backward()\n",
    "        opt_g.step()\n",
    "        \n",
    "        if i % 10 == 0: print(f\"Step {i} | Loss G: {loss_g.item():.4f}\")\n",
    "        if i > 50: break # Demo 50 bước\n",
    "    \n",
    "    # --- LƯU MODEL ---\n",
    "    save_dir = os.path.join(os.path.expanduser(\"~\"), \"cloud_train\", \"saved_models\")\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "    torch.save(gen.state_dict(), os.path.join(save_dir, \"SpAGAN_generator.pth\"))\n",
    "    print(f\"\\n💾 Đã lưu model tại: {save_dir}/SpAGAN_generator.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1d8e75-0a3f-4197-8196-7132122f2be9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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