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