src/fp_sanity_check.py

# Quick sanity check — Run this before running a single line of training loop code:
import torch
from model import SiameseNet
from loss import ContrastiveLoss

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = SiameseNet(embedding_dim=128).to(device)
criterion = ContrastiveLoss(margin=1.0)

# Fake a batch matching your DataLoader output shape
img1   = torch.randn(32, 1, 105, 105).to(device)
img2   = torch.randn(32, 1, 105, 105).to(device)
labels = torch.randint(0, 2, (32,)).float().to(device)

emb1, emb2 = model(img1, img2)
loss, dist  = criterion(emb1, emb2, labels)

print(f"emb1 shape  : {emb1.shape}")        # [32, 128]
print(f"emb2 shape  : {emb2.shape}")        # [32, 128]
print(f"emb1 norm   : {emb1.norm(dim=1).mean():.4f}")   # should be ~1.0
print(f"loss        : {loss.item():.4f}")
print(f"dist range  : {dist.min():.3f} – {dist.max():.3f}")
print("Sanity check passed")