refactor: reorganize project structure by moving core modules and update import paths in API server

scripts/training/train_ndvi_convlstm.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+# coding: utf-8
+import os
+import json
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from core.ndvi_data_loader import NDVITimeSeriesDataset
+
+print("=" * 70)
+print("🚀 Training ConvLSTM Spatial-Temporal model for NDVI (REAL DATA & GPU)")
+print("=" * 70)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"[SYSTEM] Device: {device.type.upper()}")
+
+dataset = NDVITimeSeriesDataset(sequence_length=3, spatial=True)
+dataloader = DataLoader(dataset, batch_size=2, shuffle=True)
+
+class ConvLSTMCell(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=3):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels + out_channels, 4 * out_channels, kernel_size, padding=1)
+    
+    def forward(self, x, h, c):
+        combined = torch.cat([x, h], dim=1)
+        gates = self.conv(combined)
+        i, f, o, g = torch.chunk(gates, 4, dim=1)
+        i, f, o, g = torch.sigmoid(i), torch.sigmoid(f), torch.sigmoid(o), torch.tanh(g)
+        c_next = f * c + i * g
+        h_next = o * torch.tanh(c_next)
+        return h_next, c_next
+
+class MiniConvLSTM(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.cell = ConvLSTMCell(1, 16)
+        self.out_conv = nn.Conv2d(16, 1, kernel_size=1)
+        
+    def forward(self, x):
+        B, T, C, H, W = x.shape
+        h = torch.zeros(B, 16, H, W).to(x.device)
+        c = torch.zeros(B, 16, H, W).to(x.device)
+        for t in range(T):
+            h, c = self.cell(x[:, t], h, c)
+        return self.out_conv(h)
+
+model = MiniConvLSTM().to(device)
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+print("\n[TRAIN] Bắt đầu Training...")
+model.train()
+total_loss = 0
+for epoch in range(20):
+    for inputs, targets in dataloader:
+        inputs, targets = inputs.to(device), targets.to(device)
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        total_loss += loss.item()
+
+avg_loss = total_loss / (len(dataloader) * 20)
+print(f"✅ Training completed! Avg MSE Loss (RMSE): {avg_loss**0.5:.4f}")
+
+model_dir = "ndvi_forecast_model"
+model_path = os.path.join(model_dir, "ndvi_convlstm_real.pth")
+torch.save(model.state_dict(), model_path)
+
+with open(os.path.join(model_dir, "ndvi_convlstm_real_info.json"), "w") as f:
+    json.dump({
+        "model_type": "ConvLSTM Spatial-Temporal (Real Data & GPU)",
+        "target": "NDVI", "epoch": 20,
+        "rmse": float(avg_loss**0.5), "mae": float(avg_loss)
+    }, f, indent=2)
+print("[SAVE] Model info saved.")