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Victor Phan
2026-01-24 15:48:52 +00:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c886b44b-4288-4260-8b5b-bb78a87e172f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📂 Working Directory: /home/jovyan/cloud_train/dataset\n",
"✅ Đã import thành công (dùng đường dẫn tuyệt đối)!\n",
"Using device: cpu\n",
"✅ Data Ready: 29117 samples\n"
]
}
],
"source": [
"# ==========================================\n",
"# CELL 1: SETUP & LOAD DỮ LIỆU (ĐÃ SỬA LỖI IMPORT)\n",
"# ==========================================\n",
"import os\n",
"import sys\n",
"import torch\n",
"import numpy as np\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torch.nn as nn\n",
"\n",
"# --- KHẮC PHỤC LỖI KHÔNG TÌM THẤY MODULE ---\n",
"# Thêm đường dẫn thư mục hiện tại vào hệ thống để Python tìm thấy file loader\n",
"current_dir = os.getcwd()\n",
"sys.path.append(current_dir)\n",
"print(f\"📂 Working Directory: {current_dir}\")\n",
"\n",
"try:\n",
" from sen12ms_cr_dataLoader import SEN12MSCRDataset, Seasons, S1Bands, S2Bands\n",
" print(\"✅ Đã import thành công sen12ms_cr_dataLoader!\")\n",
"except ImportError:\n",
" # Nếu vẫn lỗi, thử trỏ cứng vào thư mục cloud_train\n",
" sys.path.append(\"/home/jovyan/cloud_train\")\n",
" try:\n",
" from sen12ms_cr_dataLoader import SEN12MSCRDataset, Seasons, S1Bands, S2Bands\n",
" print(\"✅ Đã import thành công (dùng đường dẫn tuyệt đối)!\")\n",
" except ImportError:\n",
" raise RuntimeError(\"❌ Vẫn không tìm thấy file 'sen12ms_cr_dataLoader.py'. Hãy đảm bảo file này nằm cùng thư mục với Notebook!\")\n",
"\n",
"# Cấu hình thiết bị\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(f\"Using device: {device}\")\n",
"\n",
"# Đường dẫn dataset\n",
"base_dir = os.path.join(os.path.expanduser(\"~\"), \"cloud_train\", \"dataset\")\n",
"\n",
"# Dataset Wrapper\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",
"try:\n",
" train_dataset = SEN12MSCR_TorchDataset(base_dir)\n",
" train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=0)\n",
" print(f\"✅ Data Ready: {len(train_dataset)} samples\")\n",
"except Exception as e:\n",
" print(f\"❌ Lỗi Data: {e}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a6357434-8b1c-48ef-ac84-7afadc4ce04a",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# ==========================================\n",
"# CELL 2: ĐỊNH NGHĨA MODEL CR-GAN\n",
"# ==========================================\n",
"class CR_Generator(nn.Module):\n",
" def __init__(self):\n",
" super(CR_Generator, self).__init__()\n",
" # Input: 13 kênh (Mây) + 2 kênh (Radar) = 15\n",
" self.enc1 = nn.Sequential(nn.Conv2d(15, 64, 4, 2, 1), nn.LeakyReLU(0.2))\n",
" self.enc2 = nn.Sequential(nn.Conv2d(64, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2))\n",
" self.enc3 = nn.Sequential(nn.Conv2d(128, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2))\n",
" \n",
" self.dec1 = nn.Sequential(nn.ConvTranspose2d(256, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.ReLU())\n",
" self.dec2 = nn.Sequential(nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.ReLU())\n",
" self.dec3 = nn.ConvTranspose2d(64, 13, 4, 2, 1) # Output 13 kênh sạch\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",
" e2 = self.enc2(e1)\n",
" e3 = self.enc3(e2)\n",
" \n",
" d1 = self.dec1(e3)\n",
" d2 = self.dec2(d1 + e2) # Skip connection\n",
" d3 = self.dec3(d2 + e1)\n",
" return self.tanh(d3)\n",
"\n",
"class Discriminator(nn.Module):\n",
" def __init__(self):\n",
" super(Discriminator, self).__init__()\n",
" # Input: 13 (Ảnh) + 15 (Điều kiện) = 28\n",
" self.model = nn.Sequential(\n",
" nn.Conv2d(28, 64, 4, 2, 1), nn.LeakyReLU(0.2),\n",
" nn.Conv2d(64, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2),\n",
" nn.Conv2d(128, 1, 4, 1, 0), nn.Sigmoid()\n",
" )\n",
" def forward(self, img, cond1, cond2):\n",
" return self.model(torch.cat([img, cond1, cond2], 1))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09276904-a4c9-4774-b171-32d2e577afa0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🚀 Bắt đầu huấn luyện CR-GAN...\n",
"Epoch 0 | Step 0 | Loss G: 72.3185 | Loss D: 0.7102\n",
"Epoch 0 | Step 10 | Loss G: 37.1491 | Loss D: 0.5784\n",
"Epoch 0 | Step 20 | Loss G: 19.9918 | Loss D: 0.5008\n",
"Epoch 0 | Step 30 | Loss G: 21.9959 | Loss D: 0.6595\n",
"Epoch 0 | Step 40 | Loss G: 10.0374 | Loss D: 0.5188\n",
"Epoch 0 | Step 50 | Loss G: 7.7058 | Loss D: 0.5300\n",
"Epoch 0 | Step 60 | Loss G: 6.9481 | Loss D: 0.5223\n",
"Epoch 0 | Step 70 | Loss G: 12.7031 | Loss D: 0.6349\n",
"Epoch 0 | Step 80 | Loss G: 6.1611 | Loss D: 0.6098\n",
"Epoch 0 | Step 90 | Loss G: 8.4944 | Loss D: 0.4761\n",
"Epoch 0 | Step 100 | Loss G: 16.4352 | Loss D: 1.2795\n",
"Epoch 0 | Step 110 | Loss G: 6.7580 | Loss D: 0.6131\n",
"Epoch 0 | Step 120 | Loss G: 6.7856 | Loss D: 0.5403\n",
"Epoch 0 | Step 130 | Loss G: 6.2629 | Loss D: 0.6605\n",
"Epoch 0 | Step 140 | Loss G: 5.4081 | Loss D: 0.5667\n",
"Epoch 0 | Step 150 | Loss G: 5.5551 | Loss D: 0.5392\n",
"Epoch 0 | Step 160 | Loss G: 5.4969 | Loss D: 0.5394\n",
"Epoch 0 | Step 170 | Loss G: 4.6524 | Loss D: 0.5536\n",
"Epoch 0 | Step 180 | Loss G: 9.2369 | Loss D: 0.5098\n",
"Epoch 0 | Step 190 | Loss G: 10.5043 | Loss D: 0.4320\n",
"Epoch 0 | Step 200 | Loss G: 5.1155 | Loss D: 0.5958\n",
"Epoch 0 | Step 210 | Loss G: 5.1848 | Loss D: 0.5596\n",
"Epoch 0 | Step 220 | Loss G: 6.5014 | Loss D: 0.5307\n",
"Epoch 0 | Step 230 | Loss G: 6.4747 | Loss D: 0.6416\n",
"Epoch 0 | Step 240 | Loss G: 4.6687 | Loss D: 0.5795\n",
"Epoch 0 | Step 250 | Loss G: 3.8810 | Loss D: 0.6212\n",
"Epoch 0 | Step 260 | Loss G: 4.5834 | Loss D: 0.5906\n",
"Epoch 0 | Step 270 | Loss G: 6.8081 | Loss D: 0.4587\n",
"Epoch 0 | Step 280 | Loss G: 6.4570 | Loss D: 0.5759\n",
"Epoch 0 | Step 290 | Loss G: 4.5583 | Loss D: 0.5872\n",
"Epoch 0 | Step 300 | Loss G: 5.8205 | Loss D: 0.6410\n",
"Epoch 0 | Step 310 | Loss G: 4.7019 | Loss D: 0.6298\n",
"Epoch 0 | Step 320 | Loss G: 4.3386 | Loss D: 0.5507\n",
"Epoch 0 | Step 330 | Loss G: 4.2705 | Loss D: 0.5533\n",
"Epoch 0 | Step 340 | Loss G: 6.9023 | Loss D: 0.8489\n",
"Epoch 0 | Step 350 | Loss G: 8.0638 | Loss D: 0.7825\n",
"Epoch 0 | Step 360 | Loss G: 4.4931 | Loss D: 0.6148\n",
"Epoch 0 | Step 370 | Loss G: 3.7342 | Loss D: 0.6964\n",
"Epoch 0 | Step 380 | Loss G: 4.2160 | Loss D: 0.6782\n",
"Epoch 0 | Step 390 | Loss G: 5.1540 | Loss D: 0.6306\n",
"Epoch 0 | Step 400 | Loss G: 4.5333 | Loss D: 0.6755\n",
"Epoch 0 | Step 410 | Loss G: 4.0220 | Loss D: 0.6751\n",
"Epoch 0 | Step 420 | Loss G: 5.7766 | Loss D: 0.7027\n",
"Epoch 0 | Step 430 | Loss G: 4.0071 | Loss D: 0.5669\n",
"Epoch 0 | Step 440 | Loss G: 4.1375 | Loss D: 0.4732\n",
"Epoch 0 | Step 450 | Loss G: 3.8729 | Loss D: 0.5686\n",
"Epoch 0 | Step 460 | Loss G: 3.5877 | Loss D: 0.6608\n",
"Epoch 0 | Step 470 | Loss G: 5.6308 | Loss D: 0.7109\n",
"Epoch 0 | Step 480 | Loss G: 5.3286 | Loss D: 0.7127\n",
"Epoch 0 | Step 490 | Loss G: 2.9878 | Loss D: 0.6668\n",
"Epoch 0 | Step 500 | Loss G: 4.0083 | Loss D: 0.7079\n",
"Epoch 0 | Step 510 | Loss G: 3.6710 | Loss D: 0.7004\n",
"Epoch 0 | Step 520 | Loss G: 5.5583 | Loss D: 0.6757\n",
"Epoch 0 | Step 530 | Loss G: 4.2446 | Loss D: 0.6811\n",
"Epoch 0 | Step 540 | Loss G: 3.7496 | Loss D: 0.6590\n",
"Epoch 0 | Step 550 | Loss G: 7.4803 | Loss D: 0.8142\n",
"Epoch 0 | Step 560 | Loss G: 3.7535 | Loss D: 0.6432\n",
"Epoch 0 | Step 570 | Loss G: 2.8306 | Loss D: 0.5701\n",
"Epoch 0 | Step 580 | Loss G: 2.9589 | Loss D: 0.6700\n",
"Epoch 0 | Step 590 | Loss G: 4.7672 | Loss D: 0.7502\n",
"Epoch 0 | Step 600 | Loss G: 3.1664 | Loss D: 0.6552\n",
"Epoch 0 | Step 610 | Loss G: 3.2172 | Loss D: 0.6563\n",
"Epoch 0 | Step 620 | Loss G: 3.1097 | Loss D: 0.7513\n",
"Epoch 0 | Step 630 | Loss G: 3.8371 | Loss D: 0.7282\n",
"Epoch 0 | Step 640 | Loss G: 3.1871 | Loss D: 0.6991\n",
"Epoch 0 | Step 650 | Loss G: 3.4674 | Loss D: 0.6747\n",
"Epoch 0 | Step 660 | Loss G: 3.5930 | Loss D: 0.6720\n",
"Epoch 0 | Step 670 | Loss G: 2.9213 | Loss D: 0.6792\n",
"Epoch 0 | Step 680 | Loss G: 3.5470 | Loss D: 0.6509\n",
"Epoch 0 | Step 690 | Loss G: 3.6894 | Loss D: 0.6192\n",
"Epoch 0 | Step 700 | Loss G: 3.6680 | Loss D: 0.5443\n",
"Epoch 0 | Step 710 | Loss G: 3.6357 | Loss D: 0.5633\n",
"Epoch 0 | Step 720 | Loss G: 3.2239 | Loss D: 0.6394\n",
"Epoch 0 | Step 730 | Loss G: 2.5703 | Loss D: 0.6727\n",
"Epoch 0 | Step 740 | Loss G: 3.2920 | Loss D: 0.6691\n",
"Epoch 0 | Step 750 | Loss G: 3.1487 | Loss D: 0.7395\n",
"Epoch 0 | Step 760 | Loss G: 3.2808 | Loss D: 0.6791\n",
"Epoch 0 | Step 770 | Loss G: 3.6725 | Loss D: 0.7175\n",
"Epoch 0 | Step 780 | Loss G: 2.8584 | Loss D: 0.6647\n",
"Epoch 0 | Step 790 | Loss G: 3.5167 | Loss D: 0.7073\n",
"Epoch 0 | Step 800 | Loss G: 2.9515 | Loss D: 0.6595\n",
"Epoch 0 | Step 810 | Loss G: 3.0861 | Loss D: 0.7068\n",
"Epoch 0 | Step 820 | Loss G: 5.8470 | Loss D: 0.9232\n",
"Epoch 0 | Step 830 | Loss G: 3.4973 | Loss D: 0.6484\n",
"Epoch 0 | Step 840 | Loss G: 3.3402 | Loss D: 0.6405\n",
"Epoch 0 | Step 850 | Loss G: 2.9324 | Loss D: 0.6842\n",
"Epoch 0 | Step 860 | Loss G: 3.3249 | Loss D: 0.5885\n",
"Epoch 0 | Step 870 | Loss G: 3.2350 | Loss D: 0.7483\n",
"Epoch 0 | Step 880 | Loss G: 2.9857 | Loss D: 0.6709\n",
"Epoch 0 | Step 890 | Loss G: 3.1763 | Loss D: 0.5673\n",
"Epoch 0 | Step 900 | Loss G: 3.0522 | Loss D: 0.7522\n",
"Epoch 0 | Step 910 | Loss G: 2.5046 | Loss D: 0.7584\n",
"Epoch 0 | Step 920 | Loss G: 3.5277 | Loss D: 0.7532\n",
"Epoch 0 | Step 930 | Loss G: 2.7921 | Loss D: 0.6860\n",
"Epoch 0 | Step 940 | Loss G: 4.3079 | Loss D: 0.7080\n",
"Epoch 0 | Step 950 | Loss G: 2.7965 | Loss D: 0.6728\n",
"Epoch 0 | Step 960 | Loss G: 2.7622 | Loss D: 0.6773\n",
"Epoch 0 | Step 970 | Loss G: 2.8015 | Loss D: 0.6507\n",
"Epoch 0 | Step 980 | Loss G: 2.0900 | Loss D: 0.6775\n",
"Epoch 0 | Step 990 | Loss G: 2.9263 | Loss D: 0.6361\n",
"Epoch 0 | Step 1000 | Loss G: 3.5840 | Loss D: 0.6793\n",
"Epoch 0 | Step 1010 | Loss G: 3.0541 | Loss D: 0.7161\n",
"Epoch 0 | Step 1020 | Loss G: 2.6563 | Loss D: 0.6252\n",
"Epoch 0 | Step 1030 | Loss G: 2.9624 | Loss D: 0.6389\n",
"Epoch 0 | Step 1040 | Loss G: 4.0138 | Loss D: 0.5921\n",
"Epoch 0 | Step 1050 | Loss G: 3.4586 | Loss D: 0.6120\n",
"Epoch 0 | Step 1060 | Loss G: 3.6436 | Loss D: 0.5100\n",
"Epoch 0 | Step 1070 | Loss G: 2.5240 | Loss D: 0.7917\n",
"Epoch 0 | Step 1080 | Loss G: 2.9808 | Loss D: 0.5955\n",
"Epoch 0 | Step 1090 | Loss G: 3.5152 | Loss D: 0.6790\n",
"Epoch 0 | Step 1100 | Loss G: 4.8162 | Loss D: 0.9497\n",
"Epoch 0 | Step 1110 | Loss G: 4.0998 | Loss D: 0.6457\n",
"Epoch 0 | Step 1120 | Loss G: 3.7265 | Loss D: 0.7151\n",
"Epoch 0 | Step 1130 | Loss G: 2.8072 | Loss D: 0.7019\n",
"Epoch 0 | Step 1140 | Loss G: 3.8286 | Loss D: 0.7170\n",
"Epoch 0 | Step 1150 | Loss G: 2.3177 | Loss D: 0.6822\n",
"Epoch 0 | Step 1160 | Loss G: 2.3291 | Loss D: 0.6801\n",
"Epoch 0 | Step 1170 | Loss G: 2.5152 | Loss D: 0.6733\n",
"Epoch 0 | Step 1180 | Loss G: 2.1710 | Loss D: 0.6602\n",
"Epoch 0 | Step 1190 | Loss G: 3.0357 | Loss D: 0.6695\n",
"Epoch 0 | Step 1200 | Loss G: 2.9576 | Loss D: 0.6414\n",
"Epoch 0 | Step 1210 | Loss G: 3.0184 | Loss D: 0.6573\n",
"Epoch 0 | Step 1220 | Loss G: 2.2493 | Loss D: 0.7150\n",
"Epoch 0 | Step 1230 | Loss G: 2.7642 | Loss D: 0.7177\n",
"Epoch 0 | Step 1240 | Loss G: 2.4553 | Loss D: 0.6698\n",
"Epoch 0 | Step 1250 | Loss G: 2.5607 | Loss D: 0.6953\n",
"Epoch 0 | Step 1260 | Loss G: 2.9071 | Loss D: 0.6687\n",
"Epoch 0 | Step 1270 | Loss G: 2.6420 | Loss D: 0.6780\n",
"Epoch 0 | Step 1280 | Loss G: 2.6417 | Loss D: 0.6567\n",
"Epoch 0 | Step 1290 | Loss G: 2.5739 | Loss D: 0.6899\n",
"Epoch 0 | Step 1300 | Loss G: 2.9631 | Loss D: 0.6819\n",
"Epoch 0 | Step 1310 | Loss G: 2.5568 | Loss D: 0.6575\n",
"Epoch 0 | Step 1320 | Loss G: 2.2896 | Loss D: 0.6679\n",
"Epoch 0 | Step 1330 | Loss G: 2.9179 | Loss D: 0.6886\n",
"Epoch 0 | Step 1340 | Loss G: 2.8625 | Loss D: 0.6136\n",
"Epoch 0 | Step 1350 | Loss G: 2.6456 | Loss D: 0.6368\n",
"Epoch 0 | Step 1360 | Loss G: 2.9531 | Loss D: 0.6491\n",
"Epoch 0 | Step 1370 | Loss G: 2.5252 | Loss D: 0.7159\n",
"Epoch 0 | Step 1380 | Loss G: 3.9087 | Loss D: 0.7079\n",
"Epoch 0 | Step 1390 | Loss G: 2.3737 | Loss D: 0.6529\n",
"Epoch 0 | Step 1400 | Loss G: 2.3664 | Loss D: 0.6481\n",
"Epoch 0 | Step 1410 | Loss G: 2.7657 | Loss D: 0.5882\n",
"Epoch 0 | Step 1420 | Loss G: 2.7599 | Loss D: 0.6327\n",
"Epoch 0 | Step 1430 | Loss G: 2.6549 | Loss D: 0.8037\n",
"Epoch 0 | Step 1440 | Loss G: 3.3464 | Loss D: 0.5944\n",
"Epoch 0 | Step 1450 | Loss G: 2.6873 | Loss D: 0.7476\n",
"Epoch 0 | Step 1460 | Loss G: 2.5144 | Loss D: 0.6655\n",
"Epoch 0 | Step 1470 | Loss G: 3.1850 | Loss D: 0.7270\n",
"Epoch 0 | Step 1480 | Loss G: 5.0466 | Loss D: 0.6669\n",
"Epoch 0 | Step 1490 | Loss G: 2.7675 | Loss D: 0.6640\n",
"Epoch 0 | Step 1500 | Loss G: 2.3371 | Loss D: 0.6604\n",
"Epoch 0 | Step 1510 | Loss G: 2.6685 | Loss D: 0.6510\n",
"Epoch 0 | Step 1520 | Loss G: 3.0199 | Loss D: 0.6744\n",
"Epoch 0 | Step 1530 | Loss G: 2.9492 | Loss D: 0.7057\n",
"Epoch 0 | Step 1540 | Loss G: 2.8013 | Loss D: 0.7118\n",
"Epoch 0 | Step 1550 | Loss G: 2.8135 | Loss D: 0.6324\n",
"Epoch 0 | Step 1560 | Loss G: 3.1775 | Loss D: 0.6969\n",
"Epoch 0 | Step 1570 | Loss G: 3.1974 | Loss D: 0.6341\n",
"Epoch 0 | Step 1580 | Loss G: 2.4508 | Loss D: 0.6909\n",
"Epoch 0 | Step 1590 | Loss G: 3.2501 | Loss D: 0.7138\n",
"Epoch 0 | Step 1600 | Loss G: 3.5432 | Loss D: 0.7108\n",
"Epoch 0 | Step 1610 | Loss G: 2.5303 | Loss D: 0.6710\n",
"Epoch 0 | Step 1620 | Loss G: 2.8239 | Loss D: 0.6950\n",
"Epoch 0 | Step 1630 | Loss G: 2.2003 | Loss D: 0.6568\n",
"Epoch 0 | Step 1640 | Loss G: 2.6544 | Loss D: 0.7159\n",
"Epoch 0 | Step 1650 | Loss G: 2.3602 | Loss D: 0.6818\n",
"Epoch 0 | Step 1660 | Loss G: 3.1921 | Loss D: 0.6072\n",
"Epoch 0 | Step 1670 | Loss G: 2.5231 | Loss D: 0.7427\n",
"Epoch 0 | Step 1680 | Loss G: 2.9751 | Loss D: 0.6027\n",
"Epoch 0 | Step 1690 | Loss G: 2.3957 | Loss D: 0.6853\n",
"Epoch 0 | Step 1700 | Loss G: 2.5812 | Loss D: 0.6981\n",
"Epoch 0 | Step 1710 | Loss G: 2.7025 | Loss D: 0.7519\n",
"Epoch 0 | Step 1720 | Loss G: 2.9266 | Loss D: 0.6202\n",
"Epoch 0 | Step 1730 | Loss G: 2.7670 | Loss D: 0.6953\n",
"Epoch 0 | Step 1740 | Loss G: 2.7067 | Loss D: 0.5941\n",
"Epoch 0 | Step 1750 | Loss G: 3.5343 | Loss D: 0.7225\n",
"Epoch 0 | Step 1760 | Loss G: 2.4662 | Loss D: 0.6810\n",
"Epoch 0 | Step 1770 | Loss G: 2.6970 | Loss D: 0.6763\n",
"Epoch 0 | Step 1780 | Loss G: 3.1359 | Loss D: 0.6382\n",
"Epoch 0 | Step 1790 | Loss G: 2.6589 | Loss D: 0.6323\n",
"Epoch 0 | Step 1800 | Loss G: 2.6840 | Loss D: 0.6116\n",
"Epoch 0 | Step 1810 | Loss G: 3.0749 | Loss D: 0.7142\n",
"Epoch 0 | Step 1820 | Loss G: 2.4837 | Loss D: 0.7109\n",
"Epoch 0 | Step 1830 | Loss G: 2.9848 | Loss D: 0.6832\n",
"Epoch 0 | Step 1840 | Loss G: 3.1044 | Loss D: 0.5613\n",
"Epoch 0 | Step 1850 | Loss G: 2.6458 | Loss D: 1.0100\n",
"Epoch 0 | Step 1860 | Loss G: 2.4200 | Loss D: 0.7013\n",
"Epoch 0 | Step 1870 | Loss G: 2.4586 | Loss D: 0.7051\n",
"Epoch 0 | Step 1880 | Loss G: 2.3607 | Loss D: 0.7131\n",
"Epoch 0 | Step 1890 | Loss G: 2.6252 | Loss D: 0.7187\n",
"Epoch 0 | Step 1900 | Loss G: 2.4684 | Loss D: 0.6961\n",
"Epoch 0 | Step 1910 | Loss G: 2.2537 | Loss D: 0.6876\n",
"Epoch 0 | Step 1920 | Loss G: 2.4095 | Loss D: 0.6838\n",
"Epoch 0 | Step 1930 | Loss G: 2.8157 | Loss D: 0.6736\n",
"Epoch 0 | Step 1940 | Loss G: 2.4893 | Loss D: 0.6775\n",
"Epoch 0 | Step 1950 | Loss G: 3.3445 | Loss D: 0.6962\n",
"Epoch 0 | Step 1960 | Loss G: 2.2544 | Loss D: 0.6767\n",
"Epoch 0 | Step 1970 | Loss G: 2.6515 | Loss D: 0.6322\n",
"Epoch 0 | Step 1980 | Loss G: 2.2835 | Loss D: 0.6343\n",
"Epoch 0 | Step 1990 | Loss G: 2.4262 | Loss D: 0.6298\n",
"Epoch 0 | Step 2000 | Loss G: 2.7222 | Loss D: 0.6135\n",
"Epoch 0 | Step 2010 | Loss G: 2.3119 | Loss D: 0.6223\n",
"Epoch 0 | Step 2020 | Loss G: 2.8205 | Loss D: 0.6000\n",
"Epoch 0 | Step 2030 | Loss G: 2.8469 | Loss D: 0.5928\n",
"Epoch 0 | Step 2040 | Loss G: 3.0901 | Loss D: 0.6076\n",
"Epoch 0 | Step 2050 | Loss G: 2.4228 | Loss D: 0.6983\n",
"Epoch 0 | Step 2060 | Loss G: 2.7112 | Loss D: 0.7587\n",
"Epoch 0 | Step 2070 | Loss G: 2.6442 | Loss D: 0.7504\n",
"Epoch 0 | Step 2080 | Loss G: 2.4068 | Loss D: 0.7240\n",
"Epoch 0 | Step 2090 | Loss G: 2.9278 | Loss D: 0.6676\n",
"Epoch 0 | Step 2100 | Loss G: 2.2134 | Loss D: 0.7018\n",
"Epoch 0 | Step 2110 | Loss G: 2.8740 | Loss D: 0.6760\n",
"Epoch 0 | Step 2120 | Loss G: 2.4062 | Loss D: 0.6768\n",
"Epoch 0 | Step 2130 | Loss G: 2.2714 | Loss D: 0.6880\n",
"Epoch 0 | Step 2140 | Loss G: 2.3448 | Loss D: 0.6871\n",
"Epoch 0 | Step 2150 | Loss G: 2.1828 | Loss D: 0.6292\n",
"Epoch 0 | Step 2160 | Loss G: 2.4968 | Loss D: 0.6260\n",
"Epoch 0 | Step 2170 | Loss G: 2.2925 | Loss D: 0.6720\n",
"Epoch 0 | Step 2180 | Loss G: 2.1419 | Loss D: 0.6476\n",
"Epoch 0 | Step 2190 | Loss G: 2.4403 | Loss D: 0.6648\n",
"Epoch 0 | Step 2200 | Loss G: 3.3253 | Loss D: 0.5541\n",
"Epoch 0 | Step 2210 | Loss G: 2.3137 | Loss D: 0.6822\n",
"Epoch 0 | Step 2220 | Loss G: 3.3092 | Loss D: 0.6984\n",
"Epoch 0 | Step 2230 | Loss G: 2.7817 | Loss D: 0.6168\n",
"Epoch 0 | Step 2240 | Loss G: 2.4387 | Loss D: 0.6215\n",
"Epoch 0 | Step 2250 | Loss G: 2.7103 | Loss D: 0.5489\n",
"Epoch 0 | Step 2260 | Loss G: 2.8750 | Loss D: 0.7436\n",
"Epoch 0 | Step 2270 | Loss G: 3.2172 | Loss D: 0.7419\n",
"Epoch 0 | Step 2280 | Loss G: 2.2944 | Loss D: 0.7735\n",
"Epoch 0 | Step 2290 | Loss G: 2.9406 | Loss D: 0.6687\n",
"Epoch 0 | Step 2300 | Loss G: 2.3666 | Loss D: 0.7192\n",
"Epoch 0 | Step 2310 | Loss G: 2.0075 | Loss D: 0.7016\n",
"Epoch 0 | Step 2320 | Loss G: 2.5500 | Loss D: 0.7104\n",
"Epoch 0 | Step 2330 | Loss G: 2.2471 | Loss D: 0.6831\n",
"Epoch 0 | Step 2340 | Loss G: 2.2565 | Loss D: 0.6743\n",
"Epoch 0 | Step 2350 | Loss G: 2.1140 | Loss D: 0.6982\n",
"Epoch 0 | Step 2360 | Loss G: 2.2506 | Loss D: 0.6729\n",
"Epoch 0 | Step 2370 | Loss G: 2.4593 | Loss D: 0.6737\n",
"Epoch 0 | Step 2380 | Loss G: 2.3511 | Loss D: 0.6809\n",
"Epoch 0 | Step 2390 | Loss G: 2.4674 | Loss D: 0.6723\n",
"Epoch 0 | Step 2400 | Loss G: 2.7037 | Loss D: 0.6395\n",
"Epoch 0 | Step 2410 | Loss G: 2.3319 | Loss D: 0.7071\n",
"Epoch 0 | Step 2420 | Loss G: 2.4786 | Loss D: 0.6931\n",
"Epoch 0 | Step 2430 | Loss G: 3.5919 | Loss D: 0.7343\n",
"Epoch 0 | Step 2440 | Loss G: 2.3883 | Loss D: 0.7144\n",
"Epoch 0 | Step 2450 | Loss G: 2.1943 | Loss D: 0.6796\n",
"Epoch 0 | Step 2460 | Loss G: 2.6514 | Loss D: 0.7204\n",
"Epoch 0 | Step 2470 | Loss G: 2.0497 | Loss D: 0.6569\n",
"Epoch 0 | Step 2480 | Loss G: 2.3691 | Loss D: 0.7213\n",
"Epoch 0 | Step 2490 | Loss G: 2.7880 | Loss D: 0.6707\n",
"Epoch 0 | Step 2500 | Loss G: 2.9353 | Loss D: 0.7046\n",
"Epoch 0 | Step 2510 | Loss G: 2.0710 | Loss D: 0.7094\n",
"Epoch 0 | Step 2520 | Loss G: 2.1715 | Loss D: 0.6868\n",
"Epoch 0 | Step 2530 | Loss G: 2.9305 | Loss D: 0.7008\n",
"Epoch 0 | Step 2540 | Loss G: 3.2625 | Loss D: 0.7100\n",
"Epoch 0 | Step 2550 | Loss G: 2.4526 | Loss D: 0.6615\n",
"Epoch 0 | Step 2560 | Loss G: 2.1840 | Loss D: 0.6649\n",
"Epoch 0 | Step 2570 | Loss G: 2.3944 | Loss D: 0.6434\n",
"Epoch 0 | Step 2580 | Loss G: 2.3077 | Loss D: 0.6637\n",
"Epoch 0 | Step 2590 | Loss G: 2.2480 | Loss D: 0.6865\n",
"Epoch 0 | Step 2600 | Loss G: 2.2794 | Loss D: 0.6606\n",
"Epoch 0 | Step 2610 | Loss G: 2.2931 | Loss D: 0.6991\n",
"Epoch 0 | Step 2620 | Loss G: 2.1852 | Loss D: 0.6694\n",
"Epoch 0 | Step 2630 | Loss G: 2.4422 | Loss D: 0.6534\n",
"Epoch 0 | Step 2640 | Loss G: 2.4345 | Loss D: 0.7486\n",
"Epoch 0 | Step 2650 | Loss G: 2.1540 | Loss D: 0.7449\n",
"Epoch 0 | Step 2660 | Loss G: 2.2600 | Loss D: 0.6953\n",
"Epoch 0 | Step 2670 | Loss G: 2.6546 | Loss D: 0.6010\n",
"Epoch 0 | Step 2680 | Loss G: 2.1378 | Loss D: 0.7238\n",
"Epoch 0 | Step 2690 | Loss G: 2.4992 | Loss D: 0.6710\n",
"Epoch 0 | Step 2700 | Loss G: 2.4387 | Loss D: 0.6570\n",
"Epoch 0 | Step 2710 | Loss G: 2.7910 | Loss D: 0.7033\n",
"Epoch 0 | Step 2720 | Loss G: 2.0291 | Loss D: 0.6740\n",
"Epoch 0 | Step 2730 | Loss G: 2.3530 | Loss D: 0.6880\n",
"Epoch 0 | Step 2740 | Loss G: 2.4699 | Loss D: 0.6142\n",
"Epoch 0 | Step 2750 | Loss G: 2.2944 | Loss D: 0.7012\n",
"Epoch 0 | Step 2760 | Loss G: 2.3153 | Loss D: 0.6831\n",
"Epoch 0 | Step 2770 | Loss G: 1.9557 | Loss D: 0.6957\n",
"Epoch 0 | Step 2780 | Loss G: 1.9761 | Loss D: 0.7643\n",
"Epoch 0 | Step 2790 | Loss G: 2.0589 | Loss D: 0.7031\n",
"Epoch 0 | Step 2800 | Loss G: 2.1808 | Loss D: 0.6616\n",
"Epoch 0 | Step 2810 | Loss G: 3.0882 | Loss D: 0.6470\n",
"Epoch 0 | Step 2820 | Loss G: 2.2611 | Loss D: 0.7233\n",
"Epoch 0 | Step 2830 | Loss G: 2.2900 | Loss D: 0.6960\n",
"Epoch 0 | Step 2840 | Loss G: 2.8689 | Loss D: 0.6929\n",
"Epoch 0 | Step 2850 | Loss G: 2.6262 | Loss D: 0.6793\n",
"Epoch 0 | Step 2860 | Loss G: 2.0969 | Loss D: 0.6568\n",
"Epoch 0 | Step 2870 | Loss G: 2.0100 | Loss D: 0.6813\n",
"Epoch 0 | Step 2880 | Loss G: 2.4225 | Loss D: 0.6647\n",
"Epoch 0 | Step 2890 | Loss G: 2.3839 | Loss D: 0.6293\n",
"Epoch 0 | Step 2900 | Loss G: 2.2553 | Loss D: 0.6671\n",
"Epoch 0 | Step 2910 | Loss G: 2.2506 | Loss D: 0.6316\n",
"Epoch 0 | Step 2920 | Loss G: 2.2017 | Loss D: 0.6144\n",
"Epoch 0 | Step 2930 | Loss G: 2.3828 | Loss D: 0.7388\n",
"Epoch 0 | Step 2940 | Loss G: 3.6152 | Loss D: 0.5791\n",
"Epoch 0 | Step 2950 | Loss G: 2.4575 | Loss D: 0.6609\n",
"Epoch 0 | Step 2960 | Loss G: 2.5841 | Loss D: 0.8060\n",
"Epoch 0 | Step 2970 | Loss G: 2.6008 | Loss D: 0.6548\n",
"Epoch 0 | Step 2980 | Loss G: 2.9643 | Loss D: 0.6375\n",
"Epoch 0 | Step 2990 | Loss G: 2.0817 | Loss D: 0.6882\n",
"Epoch 0 | Step 3000 | Loss G: 2.4347 | Loss D: 0.7490\n",
"Epoch 0 | Step 3010 | Loss G: 2.7559 | Loss D: 0.7129\n",
"Epoch 0 | Step 3020 | Loss G: 2.1537 | Loss D: 0.6953\n",
"Epoch 0 | Step 3030 | Loss G: 2.2344 | Loss D: 0.7369\n",
"Epoch 0 | Step 3040 | Loss G: 2.3036 | Loss D: 0.6224\n",
"Epoch 0 | Step 3050 | Loss G: 2.3935 | Loss D: 0.7120\n",
"Epoch 0 | Step 3060 | Loss G: 2.3280 | Loss D: 0.6134\n",
"Epoch 0 | Step 3070 | Loss G: 2.5429 | Loss D: 0.7636\n",
"Epoch 0 | Step 3080 | Loss G: 2.3605 | Loss D: 0.6986\n",
"Epoch 0 | Step 3090 | Loss G: 2.1876 | Loss D: 0.7954\n",
"Epoch 0 | Step 3100 | Loss G: 2.3063 | Loss D: 0.6299\n",
"Epoch 0 | Step 3110 | Loss G: 2.3226 | Loss D: 0.8436\n",
"Epoch 0 | Step 3120 | Loss G: 2.9968 | Loss D: 0.7128\n",
"Epoch 0 | Step 3130 | Loss G: 2.0452 | Loss D: 0.7180\n",
"Epoch 0 | Step 3140 | Loss G: 1.9460 | Loss D: 0.7010\n",
"Epoch 0 | Step 3150 | Loss G: 2.7439 | Loss D: 0.6920\n",
"Epoch 0 | Step 3160 | Loss G: 1.8548 | Loss D: 0.7069\n",
"Epoch 0 | Step 3170 | Loss G: 2.6291 | Loss D: 0.6260\n",
"Epoch 0 | Step 3180 | Loss G: 3.1567 | Loss D: 0.6356\n",
"Epoch 0 | Step 3190 | Loss G: 1.9601 | Loss D: 0.6841\n",
"Epoch 0 | Step 3200 | Loss G: 2.3887 | Loss D: 0.6708\n",
"Epoch 0 | Step 3210 | Loss G: 2.0190 | Loss D: 0.6558\n",
"Epoch 0 | Step 3220 | Loss G: 2.5288 | Loss D: 0.6406\n",
"Epoch 0 | Step 3230 | Loss G: 2.0141 | Loss D: 0.6553\n",
"Epoch 0 | Step 3240 | Loss G: 3.5275 | Loss D: 0.6934\n",
"Epoch 0 | Step 3250 | Loss G: 2.5778 | Loss D: 0.5621\n",
"Epoch 0 | Step 3260 | Loss G: 2.4638 | Loss D: 0.6106\n",
"Epoch 0 | Step 3270 | Loss G: 3.1237 | Loss D: 0.6939\n",
"Epoch 0 | Step 3280 | Loss G: 2.8655 | Loss D: 0.6382\n",
"Epoch 0 | Step 3290 | Loss G: 2.6247 | Loss D: 0.6818\n",
"Epoch 0 | Step 3300 | Loss G: 2.2132 | Loss D: 0.6349\n",
"Epoch 0 | Step 3310 | Loss G: 2.3956 | Loss D: 0.7407\n",
"Epoch 0 | Step 3320 | Loss G: 2.8687 | Loss D: 0.4859\n",
"Epoch 0 | Step 3330 | Loss G: 2.4718 | Loss D: 0.7202\n",
"Epoch 0 | Step 3340 | Loss G: 1.9500 | Loss D: 0.6743\n",
"Epoch 0 | Step 3350 | Loss G: 2.4359 | Loss D: 0.6221\n",
"Epoch 0 | Step 3360 | Loss G: 2.1875 | Loss D: 0.7105\n",
"Epoch 0 | Step 3370 | Loss G: 2.2411 | Loss D: 0.5774\n",
"Epoch 0 | Step 3380 | Loss G: 2.2889 | Loss D: 0.6689\n",
"Epoch 0 | Step 3390 | Loss G: 2.5175 | Loss D: 0.7022\n",
"Epoch 0 | Step 3400 | Loss G: 2.1678 | Loss D: 0.6610\n",
"Epoch 0 | Step 3410 | Loss G: 1.8933 | Loss D: 0.7071\n",
"Epoch 0 | Step 3420 | Loss G: 2.1478 | Loss D: 0.6712\n",
"Epoch 0 | Step 3430 | Loss G: 2.5614 | Loss D: 0.6450\n",
"Epoch 0 | Step 3440 | Loss G: 2.5616 | Loss D: 0.6267\n",
"Epoch 0 | Step 3450 | Loss G: 2.2356 | Loss D: 0.6358\n",
"Epoch 0 | Step 3460 | Loss G: 3.6788 | Loss D: 0.6079\n",
"Epoch 0 | Step 3470 | Loss G: 2.4702 | Loss D: 0.6131\n"
]
}
],
"source": [
"# ==========================================\n",
"# CELL 3: HUẤN LUYỆN & LƯU MODEL\n",
"# ==========================================\n",
"if 'train_loader' in locals():\n",
" generator = CR_Generator().to(device)\n",
" discriminator = Discriminator().to(device)\n",
" \n",
" criterion_GAN = nn.BCELoss()\n",
" criterion_L1 = nn.L1Loss()\n",
" opt_g = torch.optim.Adam(generator.parameters(), lr=0.0002)\n",
" opt_d = torch.optim.Adam(discriminator.parameters(), lr=0.0002)\n",
"\n",
" print(\"🚀 Bắt đầu huấn luyện CR-GAN...\")\n",
" epochs = 5 # Demo chạy 5 vòng (tăng lên nếu muốn train thật)\n",
" \n",
" for epoch in range(epochs):\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",
" # --- Train Generator ---\n",
" opt_g.zero_grad()\n",
" fake = generator(s1, cloud)\n",
" pred_fake = discriminator(fake, s1, cloud)\n",
" loss_g = criterion_GAN(pred_fake, torch.ones_like(pred_fake)) + 100 * criterion_L1(fake, s2)\n",
" loss_g.backward()\n",
" opt_g.step()\n",
" \n",
" # --- Train Discriminator ---\n",
" opt_d.zero_grad()\n",
" pred_real = discriminator(s2, s1, cloud)\n",
" pred_fake_d = discriminator(fake.detach(), s1, cloud)\n",
" loss_d = 0.5 * (criterion_GAN(pred_real, torch.ones_like(pred_real)) + \n",
" criterion_GAN(pred_fake_d, torch.zeros_like(pred_fake_d)))\n",
" loss_d.backward()\n",
" opt_d.step()\n",
" \n",
" if i % 10 == 0: \n",
" print(f\"Epoch {epoch} | Step {i} | Loss G: {loss_g.item():.4f} | Loss D: {loss_d.item():.4f}\")\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(generator.state_dict(), os.path.join(save_dir, \"CRGAN_generator.pth\"))\n",
" print(f\"\\n💾 Đã lưu model tại: {save_dir}/CRGAN_generator.pth\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87873ca2-6a64-4fc9-8687-8c8555b99ed4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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