diff options
Diffstat (limited to 'training/train_cnn.py')
| -rwxr-xr-x | training/train_cnn.py | 181 |
1 files changed, 164 insertions, 17 deletions
diff --git a/training/train_cnn.py b/training/train_cnn.py index 2250e9c..16f8e7a 100755 --- a/training/train_cnn.py +++ b/training/train_cnn.py @@ -5,10 +5,15 @@ CNN Training Script for Image-to-Image Transformation Trains a convolutional neural network on multiple input/target image pairs. Usage: + # Training python3 train_cnn.py --input input_dir/ --target target_dir/ [options] + # Inference (generate ground truth) + python3 train_cnn.py --infer image.png --export-only checkpoint.pth --output result.png + Example: python3 train_cnn.py --input ./input --target ./output --layers 3 --epochs 100 + python3 train_cnn.py --infer input.png --export-only checkpoints/checkpoint_epoch_10000.pth """ import torch @@ -126,10 +131,8 @@ class SimpleCNN(nn.Module): # Final layer (grayscale output) final_input = torch.cat([out, x_coords, y_coords, gray], dim=1) - out = self.layers[-1](final_input) # [B,1,H,W] in [-1,1] - - # Denormalize to [0,1] and expand to RGB for visualization - out = (out + 1.0) * 0.5 + out = self.layers[-1](final_input) # [B,1,H,W] + out = torch.clamp(out, 0.0, 1.0) # Clip to [0,1] return out.expand(-1, 3, -1, -1) @@ -167,8 +170,6 @@ def generate_layer_shader(output_path, num_layers, kernel_sizes): f.write("}\n\n") f.write("@fragment fn fs_main(@builtin(position) p: vec4<f32>) -> @location(0) vec4<f32> {\n") f.write(" let uv = p.xy / uniforms.resolution;\n") - f.write(" let input_raw = textureSample(txt, smplr, uv);\n") - f.write(" let input = (input_raw - 0.5) * 2.0; // Normalize to [-1,1]\n") f.write(" let original_raw = textureSample(original_input, smplr, uv);\n") f.write(" let original = (original_raw - 0.5) * 2.0; // Normalize to [-1,1]\n") f.write(" var result = vec4<f32>(0.0);\n\n") @@ -180,11 +181,12 @@ def generate_layer_shader(output_path, num_layers, kernel_sizes): conv_fn = f"cnn_conv{ks}x{ks}_7to4" if not is_final else f"cnn_conv{ks}x{ks}_7to1" if layer_idx == 0: - f.write(f" // Layer 0: 7→4 (RGBD output)\n") + conv_fn_src = f"cnn_conv{ks}x{ks}_7to4_src" + f.write(f" // Layer 0: 7→4 (RGBD output, normalizes [0,1] input)\n") f.write(f" if (params.layer_index == {layer_idx}) {{\n") - f.write(f" result = {conv_fn}(txt, smplr, uv, uniforms.resolution,\n") - f.write(f" original, weights_layer{layer_idx});\n") - f.write(f" result = cnn_tanh(result); // Keep in [-1,1]\n") + f.write(f" result = {conv_fn_src}(txt, smplr, uv, uniforms.resolution,\n") + f.write(f" weights_layer{layer_idx});\n") + f.write(f" result = cnn_tanh(result);\n") f.write(f" }}\n") elif not is_final: f.write(f" else if (params.layer_index == {layer_idx}) {{\n") @@ -196,18 +198,21 @@ def generate_layer_shader(output_path, num_layers, kernel_sizes): f.write(f" else if (params.layer_index == {layer_idx}) {{\n") f.write(f" let gray_out = {conv_fn}(txt, smplr, uv, uniforms.resolution,\n") f.write(f" original, weights_layer{layer_idx});\n") - f.write(f" result = vec4<f32>(gray_out, gray_out, gray_out, 1.0); // Keep in [-1,1]\n") + f.write(f" // gray_out already in [0,1] from clipped training\n") + f.write(f" let original_denorm = (original + 1.0) * 0.5;\n") + f.write(f" result = vec4<f32>(gray_out, gray_out, gray_out, 1.0);\n") + f.write(f" let blended = mix(original_denorm, result, params.blend_amount);\n") + f.write(f" return blended; // [0,1]\n") f.write(f" }}\n") # Add else clause for invalid layer index if num_layers > 0: f.write(f" else {{\n") - f.write(f" result = input;\n") + f.write(f" return textureSample(txt, smplr, uv);\n") f.write(f" }}\n") - f.write("\n // Blend with ORIGINAL input from layer 0 and denormalize for display\n") - f.write(" let blended = mix(original, result, params.blend_amount);\n") - f.write(" return (blended + 1.0) * 0.5; // Denormalize to [0,1] for display\n") + f.write("\n // Non-final layers: denormalize for display\n") + f.write(" return (result + 1.0) * 0.5; // [-1,1] → [0,1]\n") f.write("}\n") @@ -253,6 +258,62 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): f.write(");\n\n") +def generate_conv_src_function(kernel_size, output_path): + """Generate cnn_conv{K}x{K}_7to4_src() function for layer 0""" + + k = kernel_size + num_positions = k * k + radius = k // 2 + + with open(output_path, 'a') as f: + f.write(f"\n// Source layer: 7→4 channels (RGBD output)\n") + f.write(f"// Normalizes [0,1] input to [-1,1] internally\n") + f.write(f"fn cnn_conv{k}x{k}_7to4_src(\n") + f.write(f" tex: texture_2d<f32>,\n") + f.write(f" samp: sampler,\n") + f.write(f" uv: vec2<f32>,\n") + f.write(f" resolution: vec2<f32>,\n") + f.write(f" weights: array<array<f32, 8>, {num_positions * 4}>\n") + f.write(f") -> vec4<f32> {{\n") + f.write(f" let step = 1.0 / resolution;\n\n") + + # Normalize center pixel for gray channel + f.write(f" let original = (textureSample(tex, samp, uv) - 0.5) * 2.0;\n") + f.write(f" let gray = 0.2126*original.r + 0.7152*original.g + 0.0722*original.b;\n") + f.write(f" let uv_norm = (uv - 0.5) * 2.0;\n\n") + + f.write(f" var sum = vec4<f32>(0.0);\n") + f.write(f" var pos = 0;\n\n") + + # Convolution loop + f.write(f" for (var dy = -{radius}; dy <= {radius}; dy++) {{\n") + f.write(f" for (var dx = -{radius}; dx <= {radius}; dx++) {{\n") + f.write(f" let offset = vec2<f32>(f32(dx), f32(dy)) * step;\n") + f.write(f" let rgbd = (textureSample(tex, samp, uv + offset) - 0.5) * 2.0;\n\n") + + # 7-channel input + f.write(f" let inputs = array<f32, 7>(\n") + f.write(f" rgbd.r, rgbd.g, rgbd.b, rgbd.a,\n") + f.write(f" uv_norm.x, uv_norm.y, gray\n") + f.write(f" );\n\n") + + # Accumulate + f.write(f" for (var out_c = 0; out_c < 4; out_c++) {{\n") + f.write(f" let idx = pos * 4 + out_c;\n") + f.write(f" var channel_sum = weights[idx][7];\n") + f.write(f" for (var in_c = 0; in_c < 7; in_c++) {{\n") + f.write(f" channel_sum += weights[idx][in_c] * inputs[in_c];\n") + f.write(f" }}\n") + f.write(f" sum[out_c] += channel_sum;\n") + f.write(f" }}\n") + f.write(f" pos++;\n") + f.write(f" }}\n") + f.write(f" }}\n\n") + + f.write(f" return sum;\n") + f.write(f"}}\n") + + def train(args): """Main training loop""" @@ -340,6 +401,24 @@ def train(args): print(f"Generating layer shader to {shader_path}...") generate_layer_shader(shader_path, args.layers, kernel_sizes) + # Generate _src variants for kernel sizes (skip 3x3, already exists) + for ks in set(kernel_sizes): + if ks == 3: + continue + conv_path = os.path.join(shader_dir, f'cnn_conv{ks}x{ks}.wgsl') + if not os.path.exists(conv_path): + print(f"Warning: {conv_path} not found, skipping _src generation") + continue + + # Check if _src already exists + with open(conv_path, 'r') as f: + content = f.read() + if f"cnn_conv{ks}x{ks}_7to4_src" in content: + continue + + generate_conv_src_function(ks, conv_path) + print(f"Added _src variant to {conv_path}") + print("Training complete!") @@ -372,26 +451,94 @@ def export_from_checkpoint(checkpoint_path, output_path=None): print(f"Generating layer shader to {shader_path}...") generate_layer_shader(shader_path, num_layers, kernel_sizes) + # Generate _src variants for kernel sizes (skip 3x3, already exists) + for ks in set(kernel_sizes): + if ks == 3: + continue + conv_path = os.path.join(shader_dir, f'cnn_conv{ks}x{ks}.wgsl') + if not os.path.exists(conv_path): + print(f"Warning: {conv_path} not found, skipping _src generation") + continue + + # Check if _src already exists + with open(conv_path, 'r') as f: + content = f.read() + if f"cnn_conv{ks}x{ks}_7to4_src" in content: + continue + + generate_conv_src_function(ks, conv_path) + print(f"Added _src variant to {conv_path}") + print("Export complete!") +def infer_from_checkpoint(checkpoint_path, input_path, output_path): + """Run inference on single image to generate ground truth""" + + if not os.path.exists(checkpoint_path): + print(f"Error: Checkpoint '{checkpoint_path}' not found") + sys.exit(1) + + if not os.path.exists(input_path): + print(f"Error: Input image '{input_path}' not found") + sys.exit(1) + + print(f"Loading checkpoint from {checkpoint_path}...") + checkpoint = torch.load(checkpoint_path, map_location='cpu') + + # Reconstruct model + model = SimpleCNN( + num_layers=checkpoint['num_layers'], + kernel_sizes=checkpoint['kernel_sizes'] + ) + model.load_state_dict(checkpoint['model_state']) + model.eval() + + # Load image [0,1] + print(f"Loading input image: {input_path}") + img = Image.open(input_path).convert('RGBA') + img_tensor = transforms.ToTensor()(img).unsqueeze(0) # [1,4,H,W] + + # Inference + print("Running inference...") + with torch.no_grad(): + out = model(img_tensor) # [1,3,H,W] in [0,1] + + # Save + print(f"Saving output to: {output_path}") + os.makedirs(os.path.dirname(output_path), exist_ok=True) + transforms.ToPILImage()(out.squeeze(0)).save(output_path) + print("Done!") + + def main(): parser = argparse.ArgumentParser(description='Train CNN for image-to-image transformation') - parser.add_argument('--input', help='Input image directory') + parser.add_argument('--input', help='Input image directory (training) or single image (inference)') parser.add_argument('--target', help='Target image directory') parser.add_argument('--layers', type=int, default=1, help='Number of CNN layers (default: 1)') parser.add_argument('--kernel_sizes', default='3', help='Comma-separated kernel sizes (default: 3)') parser.add_argument('--epochs', type=int, default=100, help='Number of training epochs (default: 100)') parser.add_argument('--batch_size', type=int, default=4, help='Batch size (default: 4)') parser.add_argument('--learning_rate', type=float, default=0.001, help='Learning rate (default: 0.001)') - parser.add_argument('--output', help='Output WGSL file path (default: workspaces/main/shaders/cnn/cnn_weights_generated.wgsl)') + parser.add_argument('--output', help='Output path (WGSL for training/export, PNG for inference)') parser.add_argument('--checkpoint-every', type=int, default=0, help='Save checkpoint every N epochs (default: 0 = disabled)') parser.add_argument('--checkpoint-dir', help='Checkpoint directory (default: training/checkpoints)') parser.add_argument('--resume', help='Resume from checkpoint file') parser.add_argument('--export-only', help='Export WGSL from checkpoint without training') + parser.add_argument('--infer', help='Run inference on single image (requires --export-only for checkpoint)') args = parser.parse_args() + # Inference mode + if args.infer: + checkpoint = args.export_only + if not checkpoint: + print("Error: --infer requires --export-only <checkpoint>") + sys.exit(1) + output_path = args.output or 'inference_output.png' + infer_from_checkpoint(checkpoint, args.infer, output_path) + return + # Export-only mode if args.export_only: export_from_checkpoint(args.export_only, args.output) |