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#!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = ["torch", "torchvision", "numpy", "pillow", "opencv-python"]
# ///
"""CNN v3 Training Script — U-Net + FiLM
Architecture:
enc0 Conv(20→4, 3×3) + FiLM + ReLU H×W
enc1 Conv(4→8, 3×3) + FiLM + ReLU + pool2 H/2×W/2
bottleneck Conv(8→8, 1×1) + ReLU H/4×W/4
dec1 upsample×2 + cat(enc1) Conv(16→4) + FiLM H/2×W/2
dec0 upsample×2 + cat(enc0) Conv(8→4) + FiLM H×W
output sigmoid → RGBA
FiLM MLP: Linear(5→16) → ReLU → Linear(16→40)
40 = 2 × (γ+β) for enc0(4) enc1(8) dec1(4) dec0(4)
Weight budget: ~5.4 KB f16 (fits ≤6 KB target)
"""
import argparse
import time
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from cnn_v3_utils import CNNv3Dataset, N_FEATURES
# ---------------------------------------------------------------------------
# Model
# ---------------------------------------------------------------------------
def film_apply(x: torch.Tensor, gamma: torch.Tensor, beta: torch.Tensor) -> torch.Tensor:
"""Per-channel affine: gamma*x + beta. gamma/beta: (B,C) broadcast over H,W."""
return gamma[:, :, None, None] * x + beta[:, :, None, None]
class CNNv3(nn.Module):
"""U-Net + FiLM conditioning.
enc_channels: [c0, c1] channel counts per encoder level, default [4, 8]
film_cond_dim: FiLM conditioning input size, default 5
"""
def __init__(self, enc_channels=None, film_cond_dim: int = 5):
super().__init__()
if enc_channels is None:
enc_channels = [4, 8]
assert len(enc_channels) == 2, "Only 2-level U-Net supported"
c0, c1 = enc_channels
self.enc0 = nn.Conv2d(N_FEATURES, c0, 3, padding=1)
self.enc1 = nn.Conv2d(c0, c1, 3, padding=1)
self.bottleneck = nn.Conv2d(c1, c1, 1)
self.dec1 = nn.Conv2d(c1 * 2, c0, 3, padding=1) # +skip enc1
self.dec0 = nn.Conv2d(c0 * 2, 4, 3, padding=1) # +skip enc0
film_out = 2 * (c0 + c1 + c0 + 4) # γ+β for enc0, enc1, dec1, dec0
self.film_mlp = nn.Sequential(
nn.Linear(film_cond_dim, 16),
nn.ReLU(),
nn.Linear(16, film_out),
)
self.enc_channels = enc_channels
def _split_film(self, film: torch.Tensor):
c0, c1 = self.enc_channels
parts = torch.split(film, [c0, c0, c1, c1, c0, c0, 4, 4], dim=-1)
return parts # g_enc0, b_enc0, g_enc1, b_enc1, g_dec1, b_dec1, g_dec0, b_dec0
def forward(self, feat: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
"""feat: (B,20,H,W) cond: (B,5) → (B,4,H,W) RGBA [0,1]"""
g0, b0, g1, b1, gd1, bd1, gd0, bd0 = self._split_film(self.film_mlp(cond))
skip0 = F.relu(film_apply(self.enc0(feat), g0, b0))
x = F.avg_pool2d(skip0, 2)
skip1 = F.relu(film_apply(self.enc1(x), g1, b1))
x = F.relu(self.bottleneck(F.avg_pool2d(skip1, 2)))
x = F.relu(film_apply(self.dec1(
torch.cat([F.interpolate(x, scale_factor=2, mode='nearest'), skip1], dim=1)
), gd1, bd1))
x = F.relu(film_apply(self.dec0(
torch.cat([F.interpolate(x, scale_factor=2, mode='nearest'), skip0], dim=1)
), gd0, bd0))
return torch.sigmoid(x)
# ---------------------------------------------------------------------------
# Training
# ---------------------------------------------------------------------------
def train(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
enc_channels = [int(c) for c in args.enc_channels.split(',')]
print(f"Device: {device}")
dataset = CNNv3Dataset(
dataset_dir=args.input,
input_mode=args.input_mode,
patch_size=args.patch_size,
patches_per_image=args.patches_per_image,
image_size=args.image_size,
full_image=args.full_image,
channel_dropout_p=args.channel_dropout_p,
detector=args.detector,
augment=True,
)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
num_workers=0, drop_last=False)
model = CNNv3(enc_channels=enc_channels, film_cond_dim=args.film_cond_dim).to(device)
nparams = sum(p.numel() for p in model.parameters())
print(f"Model: enc={enc_channels} film_cond_dim={args.film_cond_dim} "
f"params={nparams} (~{nparams*2/1024:.1f} KB f16)")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
ckpt_dir = Path(args.checkpoint_dir)
ckpt_dir.mkdir(parents=True, exist_ok=True)
print(f"\nTraining {args.epochs} epochs batch={args.batch_size} lr={args.lr}")
start = time.time()
avg_loss = float('nan')
for epoch in range(1, args.epochs + 1):
model.train()
epoch_loss = 0.0
n_batches = 0
for feat, cond, target in loader:
feat, cond, target = feat.to(device), cond.to(device), target.to(device)
optimizer.zero_grad()
loss = criterion(model(feat, cond), target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
n_batches += 1
avg_loss = epoch_loss / max(n_batches, 1)
print(f"\rEpoch {epoch:4d}/{args.epochs} | Loss: {avg_loss:.6f} | "
f"{time.time()-start:.0f}s", end='', flush=True)
if args.checkpoint_every > 0 and epoch % args.checkpoint_every == 0:
print()
ckpt = ckpt_dir / f"checkpoint_epoch_{epoch}.pth"
torch.save(_checkpoint(model, optimizer, epoch, avg_loss, args), ckpt)
print(f" → {ckpt}")
print()
final = ckpt_dir / f"checkpoint_epoch_{args.epochs}.pth"
torch.save(_checkpoint(model, optimizer, args.epochs, avg_loss, args), final)
print(f"Final checkpoint: {final}")
print(f"Done. {time.time()-start:.1f}s")
return model
def _checkpoint(model, optimizer, epoch, loss, args):
return {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
'config': {
'enc_channels': [int(c) for c in args.enc_channels.split(',')],
'film_cond_dim': args.film_cond_dim,
'input_mode': args.input_mode,
},
}
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def main():
p = argparse.ArgumentParser(description='Train CNN v3 (U-Net + FiLM)')
# Dataset
p.add_argument('--input', default='training/dataset',
help='Dataset root (contains full/ or simple/ subdirs)')
p.add_argument('--input-mode', default='simple', choices=['simple', 'full'],
help='simple=photo samples full=Blender G-buffer samples')
p.add_argument('--channel-dropout-p', type=float, default=0.3,
help='Dropout prob for geometric channels (default 0.3)')
# Patch / full-image mode
p.add_argument('--full-image', action='store_true',
help='Use full-image mode (resize to --image-size)')
p.add_argument('--image-size', type=int, default=256,
help='Full-image resize target (default 256)')
p.add_argument('--patch-size', type=int, default=64,
help='Patch size (default 64)')
p.add_argument('--patches-per-image', type=int, default=256,
help='Patches per image per epoch (default 256)')
p.add_argument('--detector', default='harris',
choices=['harris', 'shi-tomasi', 'fast', 'gradient', 'random'],
help='Salient point detector (default harris)')
# Model
p.add_argument('--enc-channels', default='4,8',
help='Encoder channels, comma-separated (default 4,8)')
p.add_argument('--film-cond-dim', type=int, default=5,
help='FiLM conditioning input dim (default 5)')
# Training
p.add_argument('--epochs', type=int, default=200)
p.add_argument('--batch-size', type=int, default=16)
p.add_argument('--lr', type=float, default=1e-3)
p.add_argument('--checkpoint-dir', default='checkpoints')
p.add_argument('--checkpoint-every', type=int, default=50,
help='Save checkpoint every N epochs (0=disable)')
train(p.parse_args())
if __name__ == '__main__':
main()
|
