diff options
Diffstat (limited to 'training')
| -rwxr-xr-x | training/train_cnn.py | 159 |
1 files changed, 142 insertions, 17 deletions
diff --git a/training/train_cnn.py b/training/train_cnn.py index 82f0b48..1cd6579 100755 --- a/training/train_cnn.py +++ b/training/train_cnn.py @@ -112,8 +112,6 @@ class SimpleCNN(nn.Module): else: self.layers.append(nn.Conv2d(3, 3, kernel_size=kernel_size, padding=padding, bias=True)) - self.use_residual = True - def forward(self, x): B, C, H, W = x.shape y_coords = torch.linspace(0, 1, H, device=x.device).view(1,1,H,1).expand(B,1,H,W) @@ -128,11 +126,77 @@ class SimpleCNN(nn.Module): if i < len(self.layers) - 1: out = torch.tanh(out) - if self.use_residual: - out = x + out * 0.3 return out +def generate_layer_shader(output_path, num_layers, kernel_sizes): + """Generate cnn_layer.wgsl with proper layer switches""" + + with open(output_path, 'w') as f: + f.write("// CNN layer shader - uses modular convolution snippets\n") + f.write("// Supports multi-pass rendering with residual connections\n") + f.write("// DO NOT EDIT - Generated by train_cnn.py\n\n") + f.write("@group(0) @binding(0) var smplr: sampler;\n") + f.write("@group(0) @binding(1) var txt: texture_2d<f32>;\n\n") + f.write("#include \"common_uniforms\"\n") + f.write("#include \"cnn_activation\"\n") + + # Include necessary conv functions + conv_sizes = set(kernel_sizes) + for ks in sorted(conv_sizes): + f.write(f"#include \"cnn_conv{ks}x{ks}\"\n") + f.write("#include \"cnn_weights_generated\"\n\n") + + f.write("struct CNNLayerParams {\n") + f.write(" layer_index: i32,\n") + f.write(" blend_amount: f32,\n") + f.write(" _pad: vec2<f32>,\n") + f.write("};\n\n") + f.write("@group(0) @binding(2) var<uniform> uniforms: CommonUniforms;\n") + f.write("@group(0) @binding(3) var<uniform> params: CNNLayerParams;\n") + f.write("@group(0) @binding(4) var original_input: texture_2d<f32>;\n\n") + f.write("@vertex fn vs_main(@builtin(vertex_index) i: u32) -> @builtin(position) vec4<f32> {\n") + f.write(" var pos = array<vec2<f32>, 3>(\n") + f.write(" vec2<f32>(-1.0, -1.0), vec2<f32>(3.0, -1.0), vec2<f32>(-1.0, 3.0)\n") + f.write(" );\n") + f.write(" return vec4<f32>(pos[i], 0.0, 1.0);\n") + 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 = textureSample(txt, smplr, uv);\n") + f.write(" let original = textureSample(original_input, smplr, uv);\n") + f.write(" var result = vec4<f32>(0.0);\n\n") + + # Generate layer switches + for layer_idx in range(num_layers): + ks = kernel_sizes[layer_idx] + if layer_idx == 0: + f.write(f" // Layer 0 uses coordinate-aware convolution\n") + f.write(f" if (params.layer_index == {layer_idx}) {{\n") + f.write(f" result = cnn_conv{ks}x{ks}_with_coord(txt, smplr, uv, uniforms.resolution,\n") + f.write(f" rgba_weights_layer{layer_idx}, coord_weights_layer{layer_idx}, bias_layer{layer_idx});\n") + f.write(f" result = cnn_tanh(result);\n") + f.write(f" }}\n") + else: + is_last = layer_idx == num_layers - 1 + f.write(f" {'else ' if layer_idx > 0 else ''}if (params.layer_index == {layer_idx}) {{\n") + f.write(f" result = cnn_conv{ks}x{ks}(txt, smplr, uv, uniforms.resolution,\n") + f.write(f" weights_layer{layer_idx}, bias_layer{layer_idx});\n") + if not is_last: + f.write(f" result = cnn_tanh(result);\n") + f.write(f" }}\n") + + # Add else clause for invalid layer index + if num_layers > 1: + f.write(f" else {{\n") + f.write(f" result = input;\n") + f.write(f" }}\n") + + f.write("\n // Blend with ORIGINAL input from layer 0\n") + f.write(" return mix(original, result, params.blend_amount);\n") + f.write("}\n") + + def export_weights_to_wgsl(model, output_path, kernel_sizes): """Export trained weights to WGSL format""" @@ -154,10 +218,13 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): row = pos // kw col = pos % kw f.write(" mat4x4<f32>(\n") - for out_c in range(min(4, out_ch)): + for out_c in range(4): vals = [] - for in_c in range(min(4, in_ch)): - vals.append(f"{weights[out_c, in_c, row, col]:.6f}") + for in_c in range(4): + if out_c < out_ch and in_c < in_ch: + vals.append(f"{weights[out_c, in_c, row, col]:.6f}") + else: + vals.append("0.0") f.write(f" {', '.join(vals)},\n") f.write(" )") if pos < num_positions - 1: @@ -170,7 +237,12 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): coord_w = layer.coord_weights.data.cpu().numpy() f.write(f"const coord_weights_layer{layer_idx} = mat2x4<f32>(\n") for c in range(2): - vals = [f"{coord_w[out_c, c]:.6f}" for out_c in range(min(4, coord_w.shape[0]))] + vals = [] + for out_c in range(4): + if out_c < coord_w.shape[0]: + vals.append(f"{coord_w[out_c, c]:.6f}") + else: + vals.append("0.0") f.write(f" {', '.join(vals)}") if c < 1: f.write(",\n") @@ -180,8 +252,9 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): # Export bias bias = layer.bias.data.cpu().numpy() + bias_vals = [f"{bias[i]:.6f}" if i < len(bias) else "0.0" for i in range(4)] f.write(f"const bias_layer{layer_idx} = vec4<f32>(") - f.write(", ".join([f"{b:.6f}" for b in bias[:4]])) + f.write(", ".join(bias_vals)) f.write(");\n\n") layer_idx += 1 @@ -197,10 +270,13 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): row = pos // kw col = pos % kw f.write(" mat4x4<f32>(\n") - for out_c in range(min(4, out_ch)): + for out_c in range(4): vals = [] - for in_c in range(min(4, in_ch)): - vals.append(f"{weights[out_c, in_c, row, col]:.6f}") + for in_c in range(4): + if out_c < out_ch and in_c < in_ch: + vals.append(f"{weights[out_c, in_c, row, col]:.6f}") + else: + vals.append("0.0") f.write(f" {', '.join(vals)},\n") f.write(" )") if pos < num_positions - 1: @@ -211,8 +287,9 @@ def export_weights_to_wgsl(model, output_path, kernel_sizes): # Export bias bias = layer.bias.data.cpu().numpy() + bias_vals = [f"{bias[i]:.6f}" if i < len(bias) else "0.0" for i in range(4)] f.write(f"const bias_layer{layer_idx} = vec4<f32>(") - f.write(", ".join([f"{b:.6f}" for b in bias[:4]])) + f.write(", ".join(bias_vals)) f.write(");\n\n") layer_idx += 1 @@ -293,19 +370,57 @@ def train(args): }, checkpoint_path) print(f"Saved checkpoint to {checkpoint_path}") - # Export weights + # Export weights and shader output_path = args.output or 'workspaces/main/shaders/cnn/cnn_weights_generated.wgsl' print(f"\nExporting weights to {output_path}...") os.makedirs(os.path.dirname(output_path), exist_ok=True) export_weights_to_wgsl(model, output_path, kernel_sizes) + # Generate layer shader + shader_dir = os.path.dirname(output_path) + shader_path = os.path.join(shader_dir, 'cnn_layer.wgsl') + print(f"Generating layer shader to {shader_path}...") + generate_layer_shader(shader_path, args.layers, kernel_sizes) + print("Training complete!") +def export_from_checkpoint(checkpoint_path, output_path=None): + """Export WGSL files from checkpoint without training""" + + if not os.path.exists(checkpoint_path): + print(f"Error: Checkpoint file '{checkpoint_path}' not found") + sys.exit(1) + + print(f"Loading checkpoint from {checkpoint_path}...") + checkpoint = torch.load(checkpoint_path, map_location='cpu') + + kernel_sizes = checkpoint['kernel_sizes'] + num_layers = checkpoint['num_layers'] + + # Recreate model + model = SimpleCNN(num_layers=num_layers, kernel_sizes=kernel_sizes) + model.load_state_dict(checkpoint['model_state']) + + # Export weights + output_path = output_path or 'workspaces/main/shaders/cnn/cnn_weights_generated.wgsl' + print(f"Exporting weights to {output_path}...") + os.makedirs(os.path.dirname(output_path), exist_ok=True) + export_weights_to_wgsl(model, output_path, kernel_sizes) + + # Generate layer shader + shader_dir = os.path.dirname(output_path) + shader_path = os.path.join(shader_dir, 'cnn_layer.wgsl') + print(f"Generating layer shader to {shader_path}...") + generate_layer_shader(shader_path, num_layers, kernel_sizes) + + print("Export complete!") + + def main(): parser = argparse.ArgumentParser(description='Train CNN for image-to-image transformation') - parser.add_argument('--input', required=True, help='Input image directory') - parser.add_argument('--target', required=True, help='Target image directory') + parser.add_argument('--input', help='Input image directory') + 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)') @@ -315,10 +430,20 @@ def main(): 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') args = parser.parse_args() - # Validate directories + # Export-only mode + if args.export_only: + export_from_checkpoint(args.export_only, args.output) + return + + # Validate directories for training + if not args.input or not args.target: + print("Error: --input and --target required for training (or use --export-only)") + sys.exit(1) + if not os.path.isdir(args.input): print(f"Error: Input directory '{args.input}' does not exist") sys.exit(1) |