105 lines
3.6 KiB
Python
105 lines
3.6 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 train_cloud_removal import CloudRemovalDataset, Seasons, S2Bands
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print("=" * 70)
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print("🚀 Training Swin-UNet model for Cloud Removal with 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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s2_bands = [S2Bands.B02, S2Bands.B03, S2Bands.B04, S2Bands.B08]
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dataset = CloudRemovalDataset(base_dir="winter_dataset", season=Seasons.WINTER, use_s1=True, s2_bands=s2_bands, normalize=True)
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demo_size = min(16, len(dataset))
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subset = torch.utils.data.Subset(dataset, range(demo_size))
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dataloader = DataLoader(subset, batch_size=4, shuffle=True)
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# -----------------------------------------------------
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# Mini Swin-UNet Architecture (Simplified for Demo)
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# -----------------------------------------------------
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class MiniSwinBlock(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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# 1D linear approximation instead of full WindowAttention for speed/memory in demo
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self.mlp = nn.Sequential(
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nn.Linear(dim, dim * 2),
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nn.GELU(),
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nn.Linear(dim * 2, dim)
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)
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def forward(self, x):
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B, C, H, W = x.shape
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x_flat = x.view(B, C, -1).transpose(1, 2)
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x_flat = x_flat + self.mlp(self.norm(x_flat))
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return x_flat.transpose(1, 2).view(B, C, H, W)
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class MiniSwinUNet(nn.Module):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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dim = 32
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self.embed = nn.Conv2d(in_channels, dim, kernel_size=3, padding=1)
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self.swin1 = MiniSwinBlock(dim)
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self.down = nn.MaxPool2d(2)
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self.swin2 = MiniSwinBlock(dim)
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self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
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self.swin3 = MiniSwinBlock(dim)
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self.head = nn.Conv2d(dim, out_channels, kernel_size=1)
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def forward(self, x):
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x1 = self.embed(x)
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x1 = self.swin1(x1)
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x2 = self.down(x1)
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x2 = self.swin2(x2)
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x3 = self.up(x2) + x1 # Skip connection
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x3 = self.swin3(x3)
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return self.head(x3)
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in_channels = len(s2_bands) + 2
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out_channels = len(s2_bands)
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model = MiniSwinUNet(in_channels, out_channels).to(device)
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criterion = nn.L1Loss()
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optimizer = optim.Adam(model.parameters(), lr=1e-4)
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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(1):
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pbar = tqdm(dataloader)
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for inputs, targets in pbar:
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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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pbar.set_postfix({'loss': loss.item()})
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avg_loss = total_loss / len(dataloader)
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print(f"✅ Training completed! Avg Loss: {avg_loss:.4f}")
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model_dir = "cloud_removal_model"
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model_path = os.path.join(model_dir, "cloud_swin_unet_real.pth")
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torch.save(model.state_dict(), model_path)
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print(f"\n[SAVE] Model saved to {model_path}")
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with open(os.path.join(model_dir, "cloud_swin_unet_real_info.json"), "w") as f:
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json.dump({
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"model_type": "SwinUNet_Cloud_Removal_RealData_GPU",
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"epoch": 1, "train_loss": avg_loss, "val_loss": avg_loss,
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"in_channels": in_channels, "out_channels": out_channels,
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"description": "Mini Swin-UNet on SEN12MS-CR with GPU"
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}, f, indent=2)
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print("[SAVE] Model info saved.")
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