test_setup.py

"""Quick test that model creation, dataset loading, and loss work correctly."""
import sys
import os
import numpy as np
import torch

print("=" * 60)
print("TESTING: Model + Dataset + Loss Setup")
print("=" * 60)

# Test 1: Model creation
print("\n1. Creating U-Net++ + EfficientNet-B4...")
import segmentation_models_pytorch as smp

model = smp.UnetPlusPlus(
    encoder_name="efficientnet-b4",
    encoder_weights=None,  # Don't download in test
    in_channels=1,
    classes=5,
    decoder_attention_type="scse",
)
params = sum(p.numel() for p in model.parameters())
print(f"   ✅ Model created: {params:,} parameters")

# Test 2: Forward pass
print("\n2. Forward pass test...")
x = torch.randn(1, 1, 512, 512)
with torch.no_grad():
    y = model(x)
assert y.shape == (1, 5, 512, 512), f"Wrong shape: {y.shape}"
print(f"   ✅ Input {x.shape} → Output {y.shape}")

# Test 3: Loss functions
print("\n3. Loss functions...")
dice_loss = smp.losses.DiceLoss(mode="multiclass", from_logits=True, smooth=1.0)
focal_loss = smp.losses.FocalLoss(mode="multiclass", gamma=2.0)

pred = torch.randn(2, 5, 64, 64, requires_grad=True)
target = torch.randint(0, 5, (2, 64, 64))

loss = 0.5 * dice_loss(pred, target) + 0.5 * focal_loss(pred, target)
loss.backward()
print(f"   ✅ Dice+Focal loss: {loss.item():.4f}, backward OK")

# Test 4: Label remap
print("\n4. Label remapping (30→5 classes)...")
from train import LABEL_REMAP
assert LABEL_REMAP[0] == 0   # background
assert LABEL_REMAP[9] == 1   # busbar
assert LABEL_REMAP[14] == 2  # crack
assert LABEL_REMAP[10] == 2  # crack_rbn_edge → crack
assert LABEL_REMAP[11] == 3  # inactive → dark
assert LABEL_REMAP[17] == 3  # dead_cell → dark
assert LABEL_REMAP[20] == 3  # edge_dark → dark
assert LABEL_REMAP[12] == 4  # rings → other
print(f"   ✅ Remap table verified: {np.unique(LABEL_REMAP)}")

# Test 5: Augmentation pipeline
print("\n5. Augmentation pipeline...")
from train import get_train_transforms, get_val_transforms

train_tf = get_train_transforms(512)
val_tf = get_val_transforms(512)

# Simulate a 512x512 grayscale image + mask
fake_img = np.random.randint(0, 255, (512, 512), dtype=np.uint8).astype(np.float32)
fake_mask = np.random.randint(0, 5, (512, 512), dtype=np.uint8)

aug = train_tf(image=fake_img, mask=fake_mask)
assert aug["image"].shape == torch.Size([1, 512, 512]), f"Wrong aug img shape: {aug['image'].shape}"
assert aug["mask"].shape == torch.Size([512, 512]), f"Wrong aug mask shape: {aug['mask'].shape}"
print(f"   ✅ Train transform: img {aug['image'].shape}, mask {aug['mask'].shape}")

aug = val_tf(image=fake_img, mask=fake_mask)
print(f"   ✅ Val transform: img {aug['image'].shape}, mask {aug['mask'].shape}")

# Test 6: Dataset class
print("\n6. Dataset class (with fake data)...")
import tempfile
from PIL import Image as PILImage

with tempfile.TemporaryDirectory() as tmpdir:
    img_dir = os.path.join(tmpdir, "images")
    mask_dir = os.path.join(tmpdir, "masks")
    os.makedirs(img_dir)
    os.makedirs(mask_dir)

    # Create fake RGBA image and L mask
    for i in range(3):
        img = PILImage.fromarray(np.random.randint(0, 255, (512, 512, 4), dtype=np.uint8), mode="RGBA")
        img.save(os.path.join(img_dir, f"test_{i}.png"))

        # Mask with valid E-SCDD labels
        mask_arr = np.zeros((512, 512), dtype=np.uint8)
        mask_arr[100:200, 100:200] = 14  # crack
        mask_arr[200:300, 200:300] = 11  # inactive
        mask_arr[50:60, :] = 9           # busbar
        PILImage.fromarray(mask_arr, mode="L").save(os.path.join(mask_dir, f"test_{i}.png"))

    from train import ESCDDDataset
    ds = ESCDDDataset(img_dir, mask_dir, transform=get_train_transforms(512))

    img, mask = ds[0]
    assert img.shape == (1, 512, 512), f"Wrong dataset img shape: {img.shape}"
    assert mask.shape == (512, 512), f"Wrong dataset mask shape: {mask.shape}"
    unique_classes = torch.unique(mask).tolist()
    print(f"   ✅ Dataset OK: img {img.shape}, mask {mask.shape}, classes: {unique_classes}")

    # Verify remapping
    assert 0 in unique_classes  # background
    assert 2 in unique_classes  # crack (was 14)
    assert 3 in unique_classes  # dark (was 11)
    assert 1 in unique_classes  # busbar (was 9)
    print(f"   ✅ Class remapping verified in dataset output")

# Test 7: VRAM estimate
print("\n7. VRAM estimate for RTX 4060...")
# Model params: ~20.9M × 4 bytes = 83.6 MB
# Optimizer states: ×2 = 167.2 MB
# Activations for UNet++ at 512x512, bs=4 with AMP: ~2.5-3.5 GB
# Total: ~3.0-4.0 GB → fits 8GB easily
model_bytes = params * 4 / 1e6
print(f"   Model weights: {model_bytes:.0f} MB")
print(f"   Optimizer states (AdamW): ~{model_bytes * 2:.0f} MB")
print(f"   Estimated activations (bs=4, AMP): ~3000 MB")
print(f"   Estimated total: ~{model_bytes * 3 + 3000:.0f} MB")
print(f"   RTX 4060 VRAM: 8192 MB")
print(f"   ✅ Fits with ~{8192 - model_bytes * 3 - 3000:.0f} MB headroom")

print("\n" + "=" * 60)
print("ALL TESTS PASSED ✅")
print("=" * 60)
print("\nYou can now run: python train.py")