!pip install geedim
!pip install geemap

Collecting geedim
  Downloading geedim-2.0.0-py3-none-any.whl.metadata (6.0 kB)
Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.12/dist-packages (from geedim) (2.0.2)
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Downloading geedim-2.0.0-py3-none-any.whl (73 kB)
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Installing collected packages: geedim
Successfully installed geedim-2.0.0
Requirement already satisfied: geemap in /usr/local/lib/python3.12/dist-packages (0.35.3)
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Collecting jedi>=0.16 (from ipython>=4.0.0->ipywidgets->ipyfilechooser>=0.6.0->geemap)
  Downloading jedi-0.19.2-py2.py3-none-any.whl.metadata (22 kB)
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Installing collected packages: jedi
Successfully installed jedi-0.19.2

# CELL 1: HYPER-STACK DATA LOADING
import os
import numpy as np
import rasterio
from rasterio.windows import from_bounds
import ee
import geemap
import torch
import cv2
import shutil
import time
from google.colab import drive

# 1. SETUP & AUTHENTICATION (Robust Fix)
drive.mount('/content/drive', force_remount=True)

def initialize_ee():
    try:
        ee.Initialize(project='[REDACTED_FOR_SECURITY]')
        print(" Earth Engine Initialized Successfully.")
    except Exception as e:
        print(f" Initialization failed. Triggering Authentication... ({e})")
        ee.Authenticate()
        ee.Initialize(project='[REDACTED_FOR_SECURITY]')
        print(" Earth Engine Authenticated & Initialized.")


initialize_ee()

# CONFIG
TIME_WINDOWS = [
    ('2024-10-15', '2024-11-15'), # 1. Pre-Sowing
    ('2024-11-16', '2024-12-15'), # 2. Early Growth
    ('2024-12-16', '2025-01-15'), # 3. Late Growth
    ('2025-01-16', '2025-02-15'), # 4. Peak Greenness
    ('2025-02-16', '2025-03-15'), # 5. Flowering
    ('2025-03-16', '2025-04-15')  # 6. Harvest
]
ASSET_ID = 'projects/[REDACTED_FOR_SECURITY]/assets/Punjab_Mask_2024_NEW'
PATCH_SIZE = 224

def get_hyper_satmae_data():

    if not ee.data._cloud_api_resource:
        print(" EE lost connection. Re-initializing...")
        initialize_ee()

    print(" Starting Hyper-Stack Ingestion...")
    mask_img = ee.Image(ASSET_ID)
    roi_geom = mask_img.geometry()

    # Download Mask
    mask_file = 'local_mask_hyper.tif'
    if not os.path.exists(mask_file):
        geemap.download_ee_image(mask_img, mask_file, region=roi_geom, scale=10, crs='EPSG:4326', overwrite=True)

    with rasterio.open(mask_file) as src:
        b = src.bounds
        cx, cy = (b.left + b.right)/2, (b.bottom + b.top)/2
        offset = 0.06
        window = from_bounds(cx-offset, cy-offset, cx+offset, cy+offset, src.transform)
        mask = src.read(1, window=window)
        mask = np.where(mask > 0, 1.0, 0.0).astype(np.float32)
        target_h, target_w = mask.shape
        small_roi = ee.Geometry.Rectangle([cx-offset, cy-offset, cx+offset, cy+offset], proj=str(src.crs), geodesic=False)

    stack = []

    for i, (start, end) in enumerate(TIME_WINDOWS):
        fname = f'hyper_time_{i}.tif'

        # RETRY LOGIC for robustness
        attempts = 0
        while not os.path.exists(fname) and attempts < 3:
            try:
                print(f"   Downloading Step {i+1}/{len(TIME_WINDOWS)} (Attempt {attempts+1})...")
                s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterBounds(small_roi).filterDate(start, end).median().select(['B2','B3','B4','B8','B11','B12'])
                s1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterBounds(small_roi).filterDate(start, end).mean().select(['VV','VH'])
                fused = ee.Image.cat([s2, s1]).clip(small_roi)
                geemap.download_ee_image(fused, fname, region=small_roi, scale=10, crs='EPSG:4326', overwrite=True)
            except Exception as e:
                print(f"     Error: {e}")
                attempts += 1
                time.sleep(2)


        if not os.path.exists(fname):
            print(f"   Failed to download {start}. Checking fallback...")
            if i > 0:
                print("     Copying previous month's data.")
                shutil.copy(f'hyper_time_{i-1}.tif', fname)
            else:
                raise RuntimeError(" CRITICAL: First time step failed. Cannot proceed.")

        # LOAD & PROCESS
        with rasterio.open(fname) as src:
            arr = src.read() # (8, H, W)
            arr = np.transpose(arr, (1, 2, 0)) # (H, W, 8)
            if arr.shape[:2] != (target_h, target_w):
                arr = cv2.resize(arr, (target_w, target_h), interpolation=cv2.INTER_LINEAR)

            # Normalization
            s2_bands = np.clip(arr[:,:,:6] / 5000.0, 0, 1)
            s1_bands = np.clip((arr[:,:,6:] - (-25.0)) / 25.0, 0, 1)

            # Indices
            nir = s2_bands[:, :, 3] + 1e-6
            red = s2_bands[:, :, 2] + 1e-6
            blue = s2_bands[:, :, 0] + 1e-6

            ndvi = ((nir - red) / (nir + red) + 1) / 2.0
            evi = np.clip(2.5 * ((nir - red) / (nir + 6 * red - 7.5 * blue + 1)), 0, 1)

            combined = np.concatenate([s2_bands, s1_bands, ndvi[:, :, None], evi[:, :, None]], axis=2)
            stack.append(combined)

    full_cube = np.stack(stack, axis=2)
    x_out, y_out = [], []
    stride = PATCH_SIZE

    print(" Creating Patches...")
    for y in range(0, target_h, stride):
        for x in range(0, target_w, stride):
            img_p = full_cube[y:y+stride, x:x+stride]
            mask_p = mask[y:y+stride, x:x+stride]

            if img_p.shape[0] != PATCH_SIZE or img_p.shape[1] != PATCH_SIZE: continue
            if np.isnan(img_p).any(): continue

            x_out.append(img_p)
            y_out.append(mask_p)

    X = np.array(x_out, dtype=np.float32).transpose(0, 4, 3, 1, 2) # (B, C, T, H, W)
    y = np.array(y_out, dtype=np.float32)[:, None, :, :]
    X = np.nan_to_num(X, nan=0.0)

    print(f" Hyper-Dataset Ready. Shape: {X.shape}")
    return torch.tensor(X), torch.tensor(y)

# CALL THE FUNCTION
X_data, y_data = get_hyper_satmae_data()

Mounted at /content/drive
 Earth Engine Initialized Successfully.
 Starting Hyper-Stack Ingestion...

/usr/local/lib/python3.12/dist-packages/geemap/common.py:12471: FutureWarning: 'BaseImage' is deprecated and will be removed in a future release.  Please use the 'ee.Image.gd' accessor instead.
  img = gd.download.BaseImage(image)

...tmae-2026/assets/Punjab_Mask_2024_NEW:   0%|          |0/585 tiles [00:00<?]

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# CELL 2: MODEL DEFINITION (PARTIAL FINE-TUNE: 10 Frozen / 2 Unfrozen)
import torch.nn as nn
from huggingface_hub import hf_hub_download

class SatMAEPatchEmbed(nn.Module):
    def __init__(self, in_chans=10, embed_dim=768, patch_size=16):
        super().__init__()
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
    def forward(self, x):
        B, C, T, H, W = x.shape
        x = x.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = x.reshape(B, T, -1, x.shape[-1])
        return x

class SatMAEBackbone(nn.Module):
    def __init__(self, num_frames=6, in_chans=10, embed_dim=768, depth=12, num_heads=12):
        super().__init__()
        self.patch_embed = SatMAEPatchEmbed(in_chans=in_chans, embed_dim=embed_dim)
        num_patches = (224 // 16) ** 2
        self.pos_embed = nn.Parameter(torch.zeros(1, 1, num_patches + 1, embed_dim))
        self.time_embed = nn.Parameter(torch.zeros(1, num_frames, 1, embed_dim))
        self.cls_token = nn.Parameter(torch.zeros(1, 1, 1, embed_dim))
        encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=embed_dim*4, activation="gelu", batch_first=True, norm_first=True)
        self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=depth)
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        x = self.patch_embed(x)
        B, T, N, D = x.shape
        x = x + self.time_embed
        x = x.reshape(B, T*N, D)
        spatial_pos = self.pos_embed[:, :, 1:, :].expand(B, T, -1, -1).reshape(B, T*N, D)
        x = x + spatial_pos
        cls_token = self.cls_token.expand(B, -1, -1, -1).reshape(B, 1, D) + self.pos_embed[:, :, 0, :].expand(B, 1, D)
        x = torch.cat((cls_token, x), dim=1)
        x = self.blocks(x)
        x = self.norm(x)
        return x

class SatMAESegmentation(nn.Module):
    def __init__(self, num_frames=6, embed_dim=768):
        super().__init__()
        print(f" Constructing SatMAE Hyper-Stack ({num_frames} Time Steps)...")
        self.num_frames = num_frames
        self.backbone = SatMAEBackbone(num_frames=num_frames, in_chans=10, embed_dim=embed_dim)

        try:
            print(" Adapting Google ViT Weights to 10 Channels...")
            p = hf_hub_download("google/vit-base-patch16-224", "pytorch_model.bin")
            sd = torch.load(p, map_location='cpu')
            w = sd['vit.embeddings.patch_embeddings.projection.weight']
            new_w = torch.zeros(768, 10, 16, 16)
            new_w[:, :3] = w
            new_w[:, 3:] = w.mean(dim=1, keepdim=True).repeat(1, 7, 1, 1)
            self.backbone.patch_embed.proj.weight.data = new_w
            self.backbone.patch_embed.proj.bias.data = sd['vit.embeddings.patch_embeddings.projection.bias']
            print(" Weights Adapted.")
        except:
            print(" Weights missing, using random init.")

        # --- MODIFIED FREEZING STRATEGY (PARTIAL FINE-TUNE) ---
        print(" Applying Partial Fine-Tuning Strategy...")

        # 1. Freeze EVERYTHING first (Blocks 1-12, Embeddings, Norms)
        for param in self.backbone.parameters():
            param.requires_grad = False

        # 2. Unfreeze Adapters (Input + Time)
        self.backbone.time_embed.requires_grad = True
        self.backbone.patch_embed.proj.weight.requires_grad = True

        # 3. Unfreeze LAST 2 Transformer Blocks (Deep Tuning)

        for layer in self.backbone.blocks.layers[-2:]:
            for param in layer.parameters():
                param.requires_grad = True

        # 4. Unfreeze Norm Layer
        for param in self.backbone.norm.parameters():
            param.requires_grad = True

        print("  Config: Blocks 1-10 Frozen | Blocks 11-12 Unfrozen")

        # DECODER
        self.temporal_agg = nn.Conv2d(embed_dim * num_frames, embed_dim, kernel_size=1)
        self.decoder = nn.Sequential(
            nn.Upsample(scale_factor=2), nn.Conv2d(embed_dim, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.GELU(),
            nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.GELU(),
            nn.Upsample(scale_factor=2), nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(),
            nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(),
            nn.Conv2d(32, 1, 1)
        )

    def forward(self, x):
        features = self.backbone(x)[:, 1:, :]
        B, L, D = features.shape
        features = features.view(B, self.num_frames, 14, 14, D).permute(0, 4, 1, 2, 3).flatten(1, 2)
        features = self.temporal_agg(features)
        return self.decoder(features)

# CELL 3: TRAINING
import torch
import torch.optim as optim
import torch.optim.swa_utils as swa_utils
from torch.utils.data import TensorDataset, DataLoader, random_split
from scipy.ndimage import distance_transform_edt as distance
import os
import numpy as np # Added numpy import just in case

# --- CONFIGURATION ---
# 1. DEFINE SAVE_DIR FIRST (The Fix)
SAVE_DIR = '/content/drive/MyDrive/SatMAE_LongTrain_500/'
if not os.path.exists(SAVE_DIR):
    os.makedirs(SAVE_DIR)

# 2. NOW DEFINE CHECKPOINT PATH
CHECKPOINT_PATH = SAVE_DIR + "checkpoint_hyper_partial_finetunning.pth"

RESUME = False
BATCH_SIZE = 4
EPOCHS = 500

# ... rest of the code follows ...

CHECKPOINT_PATH = SAVE_DIR + "checkpoint_hyper_partial_finetunning.pth"

# 1. TIME-SAFE AUGMENTATION
def apply_augmentation(x, y):
    if np.random.rand() > 0.5:
        x = torch.flip(x, [4]); y = torch.flip(y, [3])
    if np.random.rand() > 0.5:
        x = torch.flip(x, [3]); y = torch.flip(y, [2])
    k = np.random.randint(0, 4)
    x = torch.rot90(x, k, [3, 4]); y = torch.rot90(y, k, [2, 3])
    return x, y

# 2. LOSS FUNCTIONS (UNCHANGED)
class DiceLoss(nn.Module):
    def __init__(self, smooth=1e-6):
        super().__init__(); self.smooth = smooth
    def forward(self, inputs, targets):
        inputs = torch.sigmoid(inputs).reshape(-1)
        targets = targets.reshape(-1)
        inter = (inputs * targets).sum()
        return 1 - (2. * inter + self.smooth) / (inputs.sum() + targets.sum() + self.smooth)

class HausdorffDTLoss(nn.Module):
    def __init__(self, alpha=2.0):
        super().__init__(); self.alpha = alpha
    def forward(self, pred, gt):
        with torch.no_grad():
            gt_np = gt.cpu().numpy()
            dist_map = np.zeros_like(gt_np)
            for i in range(len(gt_np)):
                mask = (gt_np[i, 0] > 0.5).astype(np.uint8)
                if mask.sum() == 0: continue
                d_in = distance(mask); d_out = distance(1 - mask)
                dist_map[i, 0] = (d_out - d_in)
            dist_map = torch.tensor(dist_map, device=pred.device, dtype=torch.float32)
        probs = torch.sigmoid(pred)
        return torch.mean((probs - gt) ** 2 * (1 + self.alpha * torch.abs(dist_map)))

class CompoundLoss(nn.Module):
    def __init__(self):
        super().__init__(); self.dice = DiceLoss(); self.boundary = HausdorffDTLoss(alpha=2.0)
    def forward(self, p, t): return 0.7*self.dice(p, t) + 0.3*self.boundary(p, t)

# 3. SETUP MODEL & OPTIMIZER
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f" Training on: {device}")
model = SatMAESegmentation(num_frames=6, embed_dim=768).to(device)
criterion = CompoundLoss()


optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=2, eta_min=1e-6)

# SWA Config
swa_model = swa_utils.AveragedModel(model)
swa_start = 350
swa_scheduler = swa_utils.SWALR(optimizer, swa_lr=5e-5)

# Loaders
ds = TensorDataset(X_data, y_data)
tr_sz = int(0.85 * len(ds))
t_ds, v_ds = random_split(ds, [tr_sz, len(ds)-tr_sz])
train_loader = DataLoader(t_ds, BATCH_SIZE, shuffle=True)
val_loader = DataLoader(v_ds, BATCH_SIZE, shuffle=False)

# 4. RESUME LOGIC
start_epoch = 0
history = {'train_loss': [], 'val_loss': []}
best_loss = float('inf')

if RESUME and os.path.exists(CHECKPOINT_PATH):
    print(" Found Checkpoint. Resuming...")
    ckpt = torch.load(CHECKPOINT_PATH)
    model.load_state_dict(ckpt['model_state_dict'])
    optimizer.load_state_dict(ckpt['optimizer_state_dict'])
    start_epoch = ckpt['epoch'] + 1
    history = ckpt['history']
    best_loss = ckpt['best_loss']
elif not RESUME and os.path.exists(CHECKPOINT_PATH):
    print(" Force Restart: Deleting old checkpoint...")
    os.remove(CHECKPOINT_PATH)
    print(" Starting Fresh from Epoch 0.")
else:
    print(" No checkpoint found. Starting Fresh.")

# 5. TRAINING LOOP
print(f" Starting Hyper-Stack Partial Fine-Tuning ({EPOCHS} Epochs)...")

for ep in range(start_epoch, EPOCHS):
    model.train()
    train_loss = 0

    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        x, y = apply_augmentation(x, y)

        optimizer.zero_grad()
        preds = model(x)
        loss = criterion(preds, y)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()

    # Validation
    model.eval()
    val_loss = 0
    with torch.no_grad():
        for x, y in val_loader:
            x, y = x.to(device), y.to(device)
            preds = model(x)
            val_loss += criterion(preds, y).item()

    avg_t = train_loss / len(train_loader)
    avg_v = val_loss / len(val_loader)
    history['train_loss'].append(avg_t)
    history['val_loss'].append(avg_v)


    if ep >= swa_start:
        swa_model.update_parameters(model)
        swa_scheduler.step()
        lr_stat = f"SWA-LR: {swa_scheduler.get_last_lr()[0]:.1e}"
    else:
        scheduler.step()
        lr_stat = f"LR: {scheduler.get_last_lr()[0]:.1e}"

        if avg_v < best_loss:
            best_loss = avg_v
            torch.save(model.state_dict(), SAVE_DIR + "SatMAE_Hyper_Partial_Best.pth")
            print(f"   Best Model Updated (Loss: {best_loss:.4f})")


    if (ep+1) % 5 == 0:
        print(f"Ep {ep+1} | T: {avg_t:.4f} | V: {avg_v:.4f} | {lr_stat}")


    if (ep+1) % 5 == 0:
        torch.save({
            'epoch': ep,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'history': history,
            'best_loss': best_loss
        }, CHECKPOINT_PATH)
        print("   Checkpoint Saved.")

print(" Finalizing SWA Model...")
swa_utils.update_bn(train_loader, swa_model, device=device)
torch.save(swa_model.state_dict(), SAVE_DIR + "SatMAE_Hyper_Partial_SWA.pth")
print("Done.")

 Training on: cuda
 Constructing SatMAE Hyper-Stack (6 Time Steps)...
 Adapting Google ViT Weights to 10 Channels...
 Weights Adapted.
 Applying Partial Fine-Tuning Strategy...
  Config: Blocks 1-10 Frozen | Blocks 11-12 Unfrozen
 No checkpoint found. Starting Fresh.
 Starting Hyper-Stack Partial Fine-Tuning (500 Epochs)...
   Best Model Updated (Loss: 1.1799)
   Best Model Updated (Loss: 1.1194)
   Best Model Updated (Loss: 0.9007)
   Best Model Updated (Loss: 0.7571)
   Best Model Updated (Loss: 0.6437)
Ep 5 | T: 0.5609 | V: 0.6437 | LR: 9.8e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.6353)
   Best Model Updated (Loss: 0.5990)
   Best Model Updated (Loss: 0.5318)
   Best Model Updated (Loss: 0.4144)
Ep 10 | T: 0.4611 | V: 0.4144 | LR: 9.1e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.3289)
Ep 15 | T: 0.4386 | V: 0.4985 | LR: 8.0e-05
   Checkpoint Saved.
Ep 20 | T: 0.4073 | V: 0.4786 | LR: 6.6e-05
   Checkpoint Saved.
Ep 25 | T: 0.3988 | V: 0.3540 | LR: 5.1e-05
   Checkpoint Saved.
Ep 30 | T: 0.4121 | V: 0.5108 | LR: 3.5e-05
   Checkpoint Saved.
Ep 35 | T: 0.3939 | V: 0.3788 | LR: 2.1e-05
   Checkpoint Saved.
Ep 40 | T: 0.3789 | V: 0.3779 | LR: 1.0e-05
   Checkpoint Saved.
Ep 45 | T: 0.4246 | V: 0.3868 | LR: 3.4e-06
   Checkpoint Saved.
Ep 50 | T: 0.3889 | V: 0.3689 | LR: 1.0e-04
   Checkpoint Saved.
   Best Model Updated (Loss: 0.3285)
Ep 55 | T: 0.3689 | V: 0.3285 | LR: 9.9e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2839)
Ep 60 | T: 0.3751 | V: 0.4794 | LR: 9.8e-05
   Checkpoint Saved.
Ep 65 | T: 0.3558 | V: 0.3094 | LR: 9.5e-05
   Checkpoint Saved.
Ep 70 | T: 0.3452 | V: 0.2978 | LR: 9.1e-05
   Checkpoint Saved.
Ep 75 | T: 0.3341 | V: 0.3550 | LR: 8.6e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2738)
Ep 80 | T: 0.3221 | V: 0.3527 | LR: 8.0e-05
   Checkpoint Saved.
Ep 85 | T: 0.3124 | V: 0.2834 | LR: 7.3e-05
   Checkpoint Saved.
Ep 90 | T: 0.3013 | V: 0.3831 | LR: 6.6e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2709)
Ep 95 | T: 0.3360 | V: 0.3079 | LR: 5.8e-05
   Checkpoint Saved.
Ep 100 | T: 0.2965 | V: 0.2945 | LR: 5.1e-05
   Checkpoint Saved.
Ep 105 | T: 0.2864 | V: 0.2798 | LR: 4.3e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2650)
Ep 110 | T: 0.2940 | V: 0.3116 | LR: 3.5e-05
   Checkpoint Saved.
Ep 115 | T: 0.2789 | V: 0.3129 | LR: 2.8e-05
   Checkpoint Saved.
Ep 120 | T: 0.2812 | V: 0.2790 | LR: 2.1e-05
   Checkpoint Saved.
Ep 125 | T: 0.2992 | V: 0.2826 | LR: 1.5e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2634)
Ep 130 | T: 0.2686 | V: 0.2634 | LR: 1.0e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2624)
   Best Model Updated (Loss: 0.2602)
   Best Model Updated (Loss: 0.2561)
Ep 135 | T: 0.2757 | V: 0.2637 | LR: 6.4e-06
   Checkpoint Saved.
Ep 140 | T: 0.2695 | V: 0.2823 | LR: 3.4e-06
   Checkpoint Saved.
Ep 145 | T: 0.2683 | V: 0.2778 | LR: 1.6e-06
   Checkpoint Saved.
Ep 150 | T: 0.2710 | V: 0.2717 | LR: 1.0e-04
   Checkpoint Saved.
Ep 155 | T: 0.2708 | V: 0.2663 | LR: 1.0e-04
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2512)
Ep 160 | T: 0.2679 | V: 0.3821 | LR: 9.9e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2484)
   Best Model Updated (Loss: 0.2343)
Ep 165 | T: 0.2648 | V: 0.2846 | LR: 9.9e-05
   Checkpoint Saved.
Ep 170 | T: 0.2483 | V: 0.2678 | LR: 9.8e-05
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Ep 175 | T: 0.2403 | V: 0.2849 | LR: 9.6e-05
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   Best Model Updated (Loss: 0.2255)
Ep 180 | T: 0.2433 | V: 0.2816 | LR: 9.5e-05
   Checkpoint Saved.
   Best Model Updated (Loss: 0.2238)
Ep 185 | T: 0.2507 | V: 0.2372 | LR: 9.3e-05
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Ep 190 | T: 0.2154 | V: 0.2418 | LR: 9.1e-05
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Ep 195 | T: 0.2233 | V: 0.2375 | LR: 8.8e-05
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Ep 200 | T: 0.2094 | V: 0.2827 | LR: 8.6e-05
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Ep 205 | T: 0.2244 | V: 0.2774 | LR: 8.3e-05
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Ep 210 | T: 0.2076 | V: 0.2498 | LR: 8.0e-05
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   Best Model Updated (Loss: 0.2224)
   Best Model Updated (Loss: 0.2199)
Ep 215 | T: 0.2045 | V: 0.2361 | LR: 7.6e-05
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   Best Model Updated (Loss: 0.2071)
Ep 220 | T: 0.1906 | V: 0.2254 | LR: 7.3e-05
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   Best Model Updated (Loss: 0.2070)
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Ep 225 | T: 0.1965 | V: 0.2024 | LR: 6.9e-05
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Ep 230 | T: 0.2059 | V: 0.2101 | LR: 6.6e-05
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Ep 235 | T: 0.1799 | V: 0.2168 | LR: 6.2e-05
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   Best Model Updated (Loss: 0.2003)
   Best Model Updated (Loss: 0.1961)
Ep 240 | T: 0.1787 | V: 0.2143 | LR: 5.8e-05
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Ep 245 | T: 0.1864 | V: 0.2065 | LR: 5.4e-05
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Ep 250 | T: 0.1760 | V: 0.2076 | LR: 5.1e-05
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   Best Model Updated (Loss: 0.1951)
Ep 255 | T: 0.1771 | V: 0.2110 | LR: 4.7e-05
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Ep 260 | T: 0.1735 | V: 0.1948 | LR: 4.3e-05
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Ep 265 | T: 0.1696 | V: 0.2056 | LR: 3.9e-05
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   Best Model Updated (Loss: 0.1948)
Ep 270 | T: 0.1613 | V: 0.1985 | LR: 3.5e-05
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Ep 275 | T: 0.1676 | V: 0.2012 | LR: 3.2e-05
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Ep 280 | T: 0.1616 | V: 0.1928 | LR: 2.8e-05
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   Best Model Updated (Loss: 0.1899)
Ep 285 | T: 0.1603 | V: 0.1946 | LR: 2.5e-05
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Ep 290 | T: 0.1610 | V: 0.1975 | LR: 2.1e-05
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Ep 295 | T: 0.1626 | V: 0.1953 | LR: 1.8e-05
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Ep 300 | T: 0.1598 | V: 0.1905 | LR: 1.5e-05
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   Best Model Updated (Loss: 0.1868)
Ep 305 | T: 0.1563 | V: 0.1946 | LR: 1.3e-05
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Ep 310 | T: 0.1552 | V: 0.1893 | LR: 1.0e-05
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   Best Model Updated (Loss: 0.1857)
Ep 315 | T: 0.1576 | V: 0.1872 | LR: 8.3e-06
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Ep 320 | T: 0.1590 | V: 0.1927 | LR: 6.4e-06
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   Best Model Updated (Loss: 0.1843)
Ep 325 | T: 0.1536 | V: 0.1891 | LR: 4.8e-06
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Ep 330 | T: 0.1527 | V: 0.1940 | LR: 3.4e-06
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Ep 335 | T: 0.1521 | V: 0.1949 | LR: 2.4e-06
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Ep 340 | T: 0.1523 | V: 0.1914 | LR: 1.6e-06
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Ep 345 | T: 0.1546 | V: 0.1904 | LR: 1.2e-06
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Ep 350 | T: 0.1568 | V: 0.1887 | LR: 1.0e-04
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Ep 355 | T: 0.1702 | V: 0.2530 | SWA-LR: 7.5e-05
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Ep 360 | T: 0.1566 | V: 0.2320 | SWA-LR: 5.0e-05
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Ep 365 | T: 0.1516 | V: 0.2116 | SWA-LR: 5.0e-05
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Ep 370 | T: 0.1604 | V: 0.1910 | SWA-LR: 5.0e-05
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Ep 375 | T: 0.1468 | V: 0.1853 | SWA-LR: 5.0e-05
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Ep 380 | T: 0.1480 | V: 0.1861 | SWA-LR: 5.0e-05
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Ep 385 | T: 0.1445 | V: 0.1832 | SWA-LR: 5.0e-05
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Ep 390 | T: 0.1426 | V: 0.1835 | SWA-LR: 5.0e-05
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Ep 395 | T: 0.1486 | V: 0.1837 | SWA-LR: 5.0e-05
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Ep 400 | T: 0.1393 | V: 0.1866 | SWA-LR: 5.0e-05
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Ep 405 | T: 0.1360 | V: 0.1813 | SWA-LR: 5.0e-05
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Ep 410 | T: 0.1357 | V: 0.1883 | SWA-LR: 5.0e-05
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Ep 415 | T: 0.1336 | V: 0.1802 | SWA-LR: 5.0e-05
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Ep 420 | T: 0.1292 | V: 0.1731 | SWA-LR: 5.0e-05
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Ep 425 | T: 0.1319 | V: 0.1898 | SWA-LR: 5.0e-05
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Ep 430 | T: 0.1339 | V: 0.1920 | SWA-LR: 5.0e-05
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Ep 435 | T: 0.1263 | V: 0.1877 | SWA-LR: 5.0e-05
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Ep 440 | T: 0.1288 | V: 0.1824 | SWA-LR: 5.0e-05
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Ep 445 | T: 0.1235 | V: 0.1784 | SWA-LR: 5.0e-05
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Ep 450 | T: 0.1223 | V: 0.1769 | SWA-LR: 5.0e-05
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Ep 455 | T: 0.1209 | V: 0.1756 | SWA-LR: 5.0e-05
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Ep 460 | T: 0.1194 | V: 0.1864 | SWA-LR: 5.0e-05
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Ep 465 | T: 0.1168 | V: 0.1765 | SWA-LR: 5.0e-05
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Ep 470 | T: 0.1186 | V: 0.1885 | SWA-LR: 5.0e-05
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Ep 475 | T: 0.1138 | V: 0.1765 | SWA-LR: 5.0e-05
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Ep 480 | T: 0.1149 | V: 0.1722 | SWA-LR: 5.0e-05
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Ep 485 | T: 0.1144 | V: 0.1697 | SWA-LR: 5.0e-05
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Ep 490 | T: 0.1138 | V: 0.1709 | SWA-LR: 5.0e-05
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Ep 495 | T: 0.1125 | V: 0.1669 | SWA-LR: 5.0e-05
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Ep 500 | T: 0.1106 | V: 0.1775 | SWA-LR: 5.0e-05
   Checkpoint Saved.
 Finalizing SWA Model...
Done.

import matplotlib.pyplot as plt
import numpy as np
import torch
import os

# --- CONFIGURATION ---
CHECKPOINT_PATH = SAVE_DIR + "checkpoint_hyper_partial_finetunning.pth"

# 1. FUNCTION TO PLOT LOSS CURVES
def plot_training_curves(checkpoint_path):
    print(f" Loading History from: {checkpoint_path}")

    if not os.path.exists(checkpoint_path):
        print(" Checkpoint not found. Cannot plot history.")
        return

    # Load checkpoint
    ckpt = torch.load(checkpoint_path, map_location='cpu')
    history = ckpt.get('history', None)

    if history is None:
        print(" No history found in checkpoint.")
        return

    train_loss = history['train_loss']
    val_loss = history['val_loss']
    epochs = range(1, len(train_loss) + 1)

    # Plotting
    plt.figure(figsize=(10, 5))
    plt.plot(epochs, train_loss, 'b-', label='Training Loss')
    plt.plot(epochs, val_loss, 'r-', label='Validation Loss')
    plt.title('Training vs. Validation Loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss (Compound)')
    plt.legend()
    plt.grid(True)
    plt.show()

# 2. FUNCTION TO VISUALIZE SAMPLES (Input vs Truth vs Pred)
def visualize_predictions(model, loader, device, num_samples=3):
    print(" Generating Sample Predictions...")
    model.eval()

    # Get one batch
    x, y = next(iter(loader))
    x, y = x.to(device), y.to(device)

    # Run Inference
    with torch.no_grad():
        preds = model(x)
        probs = torch.sigmoid(preds)
        pred_masks = (probs > 0.5).float().cpu().numpy()

    # Prepare data for plotting
    # x shape: (Batch, Channels, Time, H, W) -> We want (Batch, Channels, H, W) for T=3
    x_np = x.cpu().numpy()
    y_np = y.cpu().numpy()

    # Create Plot Grid
    fig, axs = plt.subplots(num_samples, 3, figsize=(15, 5 * num_samples))
    plt.suptitle("Model Predictions: Input vs. Ground Truth vs. Prediction", fontsize=16)

    for i in range(num_samples):
        # A. False Color Composite (NIR, Red, Green)
        # Assuming Time Step 3 is "Peak Greenness" or similar. Adjust index if needed.
        t_idx = min(3, x_np.shape[2] - 1)

        # Channels: 3=NIR, 2=Red, 1=Green (Indices might vary based on your specific stack order)
        # If your stack is [B2, B3, B4, B8...], then: 0=Blue, 1=Green, 2=Red, 3=NIR.
        nir = x_np[i, 3, t_idx, :, :]
        red = x_np[i, 2, t_idx, :, :]
        grn = x_np[i, 1, t_idx, :, :]

        # Stack and Normalize
        img = np.stack([nir, red, grn], axis=2)
        img = (img - img.min()) / (img.max() - img.min() + 1e-6)

        # B. Ground Truth
        gt = y_np[i, 0, :, :]

        # C. Prediction
        pred = pred_masks[i, 0, :, :]

        # Plot 1: Satellite Image
        axs[i, 0].imshow(img)
        axs[i, 0].set_title("Satellite Input (False Color)")
        axs[i, 0].axis('off')

        # Plot 2: Ground Truth
        axs[i, 1].imshow(gt, cmap='gray')
        axs[i, 1].set_title("Ground Truth (Target)")
        axs[i, 1].axis('off')

        # Plot 3: Prediction
        axs[i, 2].imshow(pred, cmap='gray')
        axs[i, 2].set_title("Model Prediction")
        axs[i, 2].axis('off')

    plt.tight_layout()
    plt.show()

# --- EXECUTE BOTH ---

# 1. Plot the Graph
plot_training_curves(CHECKPOINT_PATH)

# 2. Visualize the Images (Load model first)
# Re-initialize architecture to match checkpoint
vis_model = SatMAESegmentation(num_frames=6, embed_dim=768).to(device)
ckpt = torch.load(CHECKPOINT_PATH, map_location=device)
vis_model.load_state_dict(ckpt['model_state_dict'])

visualize_predictions(vis_model, val_loader, device, num_samples=3)

📊 Loading History from: /content/drive/MyDrive/SatMAE_LongTrain_500/checkpoint_hyper_partial_finetunning.pth

 Constructing SatMAE Hyper-Stack (6 Time Steps)...
 Adapting Google ViT Weights to 10 Channels...

/usr/local/lib/python3.12/dist-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True
  warnings.warn(

 Weights Adapted.
 Applying Partial Fine-Tuning Strategy...
  Config: Blocks 1-10 Frozen | Blocks 11-12 Unfrozen
🖼️ Generating Sample Predictions...

import time
import torch
import numpy as np
from scipy.spatial.distance import directed_hausdorff
from scipy.ndimage import distance_transform_edt
from sklearn.metrics import confusion_matrix

# --- CONFIGURATION ---
MODEL_TO_EVAL = "Best" # Options: "Best" or "SWA"
if MODEL_TO_EVAL == "SWA":
    LOAD_PATH = SAVE_DIR + "SatMAE_Hyper_Partial_SWA.pth"
else:
    LOAD_PATH = SAVE_DIR + "SatMAE_Hyper_Partial_Best.pth"

# --- HELPER FUNCTIONS ---
def compute_boundary_iou(pred_mask, gt_mask, dilation=2):
    """
    Computes IoU specifically along the edges of the fields.
    """
    # Create Boundary Maps using simple morphological gradients
    from scipy.ndimage import binary_dilation

    # Get edges
    pred_edges = binary_dilation(pred_mask, iterations=dilation) ^ pred_mask
    gt_edges = binary_dilation(gt_mask, iterations=dilation) ^ gt_mask

    # Calculate IoU on edges
    intersection = (pred_edges & gt_edges).sum()
    union = (pred_edges | gt_edges).sum()

    if union == 0: return 1.0 # Perfect match (both empty)
    return intersection / union

def compute_hausdorff_distance(pred_mask, gt_mask):
    """
    Computes 95th Percentile Hausdorff Distance (Robust to outliers).
    """
    if pred_mask.sum() == 0 or gt_mask.sum() == 0:
        return 0.0 # Edge case handling

    # Find coordinates of all '1' pixels
    pred_coords = np.argwhere(pred_mask)
    gt_coords = np.argwhere(gt_mask)

    # Calculate directed distances (A->B and B->A)
    d_forward = directed_hausdorff(pred_coords, gt_coords)[0]
    d_backward = directed_hausdorff(gt_coords, pred_coords)[0]

    return max(d_forward, d_backward)

# --- EVALUATION LOOP ---
def evaluate_model_depth(loader, model_path):
    print(f" Starting Deep Evaluation on: {model_path}")

    # 1. Load Model
    model = SatMAESegmentation(num_frames=6, embed_dim=768).to(device)
    checkpoint = torch.load(model_path, map_location=device)

    # Robust loading (Handles dict vs state_dict)
    if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
        model.load_state_dict(checkpoint['model_state_dict'])
    else:
        model.load_state_dict(checkpoint)

    model.eval()

    # 2. Metrics Accumulators
    tp_total, fp_total, fn_total, tn_total = 0, 0, 0, 0
    boundary_ious = []
    hausdorff_dists = []
    inference_times = []

    print("   Processing Validation Batches...", end="")

    with torch.no_grad():
        for i, (x, y) in enumerate(loader):
            x = x.to(device)
            y_true = y.cpu().numpy() # (B, 1, H, W)

            # Speed Test
            start_time = time.time()
            preds = model(x)
            torch.cuda.synchronize() if torch.cuda.is_available() else None
            end_time = time.time()
            inference_times.append((end_time - start_time) / x.shape[0]) # Time per image

            # Convert to Binary Masks
            probs = torch.sigmoid(preds)
            y_pred = (probs > 0.5).float().cpu().numpy()

            # --- GLOBAL METRICS (Pixel-wise) ---
            # Flatten for confusion matrix
            y_true_flat = y_true.flatten().astype(int)
            y_pred_flat = y_pred.flatten().astype(int)

            tn, fp, fn, tp = confusion_matrix(y_true_flat, y_pred_flat, labels=[0, 1]).ravel()
            tp_total += tp; fp_total += fp; fn_total += fn; tn_total += tn

            # --- SHAPE METRICS (Image-wise) ---
            # We must loop through the batch because Boundary/Hausdorff are per-image
            for b in range(x.shape[0]):
                p_m = y_pred[b, 0].astype(bool)
                g_m = y_true[b, 0].astype(bool)

                # Boundary IoU
                boundary_ious.append(compute_boundary_iou(p_m, g_m))

                # Hausdorff (Only if both have pixels, else skip or penalize)
                if p_m.sum() > 0 and g_m.sum() > 0:
                    hausdorff_dists.append(compute_hausdorff_distance(p_m, g_m))

            if i % 10 == 0: print(".", end="")

    print("\n Evaluation Complete.")

    # 3. CALCULATE FINAL SCORES
    epsilon = 1e-6

    # Pixel-Based
    pixel_acc = (tp_total + tn_total) / (tp_total + tn_total + fp_total + fn_total + epsilon)
    iou = tp_total / (tp_total + fp_total + fn_total + epsilon)
    precision = tp_total / (tp_total + fp_total + epsilon)
    recall = tp_total / (tp_total + fn_total + epsilon)
    f1 = 2 * (precision * recall) / (precision + recall + epsilon)

    # Shape-Based
    avg_boundary_iou = np.mean(boundary_ious)
    avg_hausdorff = np.mean(hausdorff_dists)

    # Performance
    avg_time_per_img = np.mean(inference_times)
    fps = 1.0 / avg_time_per_img

    # 4. PRINT REPORT
    print("\n" + "="*40)
    print(f" FINAL METRICS REPORT: {MODEL_TO_EVAL} MODEL")
    print("="*40)

    results = {
        "Model": f"SatMAE_Partial_{MODEL_TO_EVAL}",
        "Pixel_Accuracy": round(pixel_acc, 4),
        "IoU_Score": round(iou, 4),
        "F1_Score": round(f1, 4),
        "Precision": round(precision, 4),
        "Recall": round(recall, 4),
        "Boundary_IoU": round(avg_boundary_iou, 4),
        "Hausdorff_Dist_px": round(avg_hausdorff, 2),
        "FPS": round(fps, 2),
        "Confusion_Matrix": {
            "TP": int(tp_total), "FP": int(fp_total),
            "FN": int(fn_total), "TN": int(tn_total)
        }
    }

    import json
    print(json.dumps(results, indent=4))
    return results

# --- RUN EVALUATION ---
final_metrics = evaluate_model_depth(val_loader, LOAD_PATH)

 Starting Deep Evaluation on: /content/drive/MyDrive/SatMAE_LongTrain_500/SatMAE_Hyper_Partial_Best.pth
 Constructing SatMAE Hyper-Stack (6 Time Steps)...

/usr/local/lib/python3.12/dist-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True
  warnings.warn(

 Adapting Google ViT Weights to 10 Channels...
 Weights Adapted.
 Applying Partial Fine-Tuning Strategy...
  Config: Blocks 1-10 Frozen | Blocks 11-12 Unfrozen
   Processing Validation Batches....
 Evaluation Complete.

========================================
 FINAL METRICS REPORT: Best MODEL
========================================
{
    "Model": "SatMAE_Partial_Best",
    "Pixel_Accuracy": 0.9102,
    "IoU_Score": 0.8625,
    "F1_Score": 0.9262,
    "Precision": 0.9264,
    "Recall": 0.9259,
    "Boundary_IoU": 0.2948,
    "Hausdorff_Dist_px": 17.63,
    "FPS": 10.82,
    "Confusion_Matrix": {
        "TP": 113079,
        "FP": 8979,
        "FN": 9051,
        "TN": 69595
    }
}