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#!/usr/bin/env python3
"""CNN v2 Training Script - Parametric Static Features
Trains a multi-layer CNN with 7D static feature input:
- RGBD (4D)
- UV coordinates (2D)
- sin(10*uv.x) position encoding (1D)
- Bias dimension (1D, always 1.0)
"""
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from pathlib import Path
from PIL import Image
import time
def compute_static_features(rgb, depth=None):
"""Generate 7D static features + bias dimension.
Args:
rgb: (H, W, 3) RGB image [0, 1]
depth: (H, W) depth map [0, 1], optional
Returns:
(H, W, 8) static features tensor
"""
h, w = rgb.shape[:2]
# RGBD channels
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
d = depth if depth is not None else np.zeros((h, w), dtype=np.float32)
# UV coordinates (normalized [0, 1])
uv_x = np.linspace(0, 1, w)[None, :].repeat(h, axis=0).astype(np.float32)
uv_y = np.linspace(0, 1, h)[:, None].repeat(w, axis=1).astype(np.float32)
# Multi-frequency position encoding
sin10_x = np.sin(10.0 * uv_x).astype(np.float32)
# Bias dimension (always 1.0)
bias = np.ones((h, w), dtype=np.float32)
# Stack: [R, G, B, D, uv.x, uv.y, sin10_x, bias]
features = np.stack([r, g, b, d, uv_x, uv_y, sin10_x, bias], axis=-1)
return features
class CNNv2(nn.Module):
"""CNN v2 with parametric static features."""
def __init__(self, kernels=[1, 3, 5], channels=[16, 8, 4]):
super().__init__()
self.kernels = kernels
self.channels = channels
# Input layer: 8D (7 features + bias) → channels[0]
self.layer0 = nn.Conv2d(8, channels[0], kernel_size=kernels[0],
padding=kernels[0]//2, bias=False)
# Inner layers: (8 + C_prev) → C_next
in_ch_1 = 8 + channels[0]
self.layer1 = nn.Conv2d(in_ch_1, channels[1], kernel_size=kernels[1],
padding=kernels[1]//2, bias=False)
# Output layer: (8 + C_last) → 4 (RGBA)
in_ch_2 = 8 + channels[1]
self.layer2 = nn.Conv2d(in_ch_2, 4, kernel_size=kernels[2],
padding=kernels[2]//2, bias=False)
def forward(self, static_features):
"""Forward pass with static feature concatenation.
Args:
static_features: (B, 8, H, W) static features
Returns:
(B, 4, H, W) RGBA output [0, 1]
"""
# Layer 0: Use full 8D static features
x0 = self.layer0(static_features)
x0 = F.relu(x0)
# Layer 1: Concatenate static + layer0 output
x1_input = torch.cat([static_features, x0], dim=1)
x1 = self.layer1(x1_input)
x1 = F.relu(x1)
# Layer 2: Concatenate static + layer1 output
x2_input = torch.cat([static_features, x1], dim=1)
output = self.layer2(x2_input)
return torch.sigmoid(output)
class ImagePairDataset(Dataset):
"""Dataset of input/target image pairs."""
def __init__(self, input_dir, target_dir):
self.input_paths = sorted(Path(input_dir).glob("*.png"))
self.target_paths = sorted(Path(target_dir).glob("*.png"))
assert len(self.input_paths) == len(self.target_paths), \
f"Mismatch: {len(self.input_paths)} inputs vs {len(self.target_paths)} targets"
def __len__(self):
return len(self.input_paths)
def __getitem__(self, idx):
# Load images
input_img = np.array(Image.open(self.input_paths[idx]).convert('RGB')) / 255.0
target_img = np.array(Image.open(self.target_paths[idx]).convert('RGB')) / 255.0
# Compute static features
static_feat = compute_static_features(input_img.astype(np.float32))
# Convert to tensors (C, H, W)
static_feat = torch.from_numpy(static_feat).permute(2, 0, 1)
target = torch.from_numpy(target_img.astype(np.float32)).permute(2, 0, 1)
# Pad target to 4 channels (RGBA)
target = F.pad(target, (0, 0, 0, 0, 0, 1), value=1.0)
return static_feat, target
def train(args):
"""Train CNN v2 model."""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Training on {device}")
# Create dataset
dataset = ImagePairDataset(args.input, args.target)
dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
print(f"Loaded {len(dataset)} image pairs")
# Create model
model = CNNv2(kernels=args.kernel_sizes, channels=args.channels).to(device)
total_params = sum(p.numel() for p in model.parameters())
print(f"Model: {args.channels} channels, {args.kernel_sizes} kernels, {total_params} weights")
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
# Training loop
print(f"\nTraining for {args.epochs} epochs...")
start_time = time.time()
for epoch in range(1, args.epochs + 1):
model.train()
epoch_loss = 0.0
for static_feat, target in dataloader:
static_feat = static_feat.to(device)
target = target.to(device)
optimizer.zero_grad()
output = model(static_feat)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(dataloader)
if epoch % 100 == 0 or epoch == 1:
elapsed = time.time() - start_time
print(f"Epoch {epoch:4d}/{args.epochs} | Loss: {avg_loss:.6f} | Time: {elapsed:.1f}s")
# Save checkpoint
if args.checkpoint_every > 0 and epoch % args.checkpoint_every == 0:
checkpoint_path = Path(args.checkpoint_dir) / f"checkpoint_epoch_{epoch}.pth"
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': avg_loss,
'config': {
'kernels': args.kernel_sizes,
'channels': args.channels,
'features': ['R', 'G', 'B', 'D', 'uv.x', 'uv.y', 'sin10_x', 'bias']
}
}, checkpoint_path)
print(f" → Saved checkpoint: {checkpoint_path}")
print(f"\nTraining complete! Total time: {time.time() - start_time:.1f}s")
return model
def main():
parser = argparse.ArgumentParser(description='Train CNN v2 with parametric static features')
parser.add_argument('--input', type=str, required=True, help='Input images directory')
parser.add_argument('--target', type=str, required=True, help='Target images directory')
parser.add_argument('--kernel-sizes', type=int, nargs=3, default=[1, 3, 5],
help='Kernel sizes for 3 layers (default: 1 3 5)')
parser.add_argument('--channels', type=int, nargs=3, default=[16, 8, 4],
help='Output channels for 3 layers (default: 16 8 4)')
parser.add_argument('--epochs', type=int, default=5000, help='Training epochs')
parser.add_argument('--batch-size', type=int, default=16, help='Batch size')
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--checkpoint-dir', type=str, default='checkpoints',
help='Checkpoint directory')
parser.add_argument('--checkpoint-every', type=int, default=1000,
help='Save checkpoint every N epochs (0 = disable)')
args = parser.parse_args()
train(args)
if __name__ == '__main__':
main()
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