1 files changed, 0 insertions, 214 deletions
diff --git a/training/export_cnn_v2_shader.py b/training/export_cnn_v2_shader.py
deleted file mode 100755
index 1c74ad0..0000000
--- a/training/export_cnn_v2_shader.py
+++ /dev/null
@@ -1,214 +0,0 @@
-#!/usr/bin/env python3
-"""CNN v2 Shader Export Script - Uniform 12D→4D Architecture
-
-Converts PyTorch checkpoints to WGSL compute shaders with f16 weights.
-Generates one shader per layer with embedded weight arrays.
-
-Note: Storage buffer approach (export_cnn_v2_weights.py) is preferred for size.
-      This script is for debugging/testing with per-layer shaders.
-"""
-
-import argparse
-import numpy as np
-import torch
-from pathlib import Path
-
-
-def export_layer_shader(layer_idx, weights, kernel_size, output_dir, mip_level=0, is_output_layer=False):
-    """Generate WGSL compute shader for a single CNN layer.
-
-    Args:
-        layer_idx: Layer index (0, 1, 2, ...)
-        weights: (4, 12, k, k) weight tensor (uniform 12D→4D)
-        kernel_size: Kernel size (3, 5, etc.)
-        output_dir: Output directory path
-        mip_level: Mip level used for p0-p3 (0=original, 1=half, etc.)
-        is_output_layer: True if this is the final RGBA output layer
-    """
-    weights_flat = weights.flatten()
-    weights_f16 = weights_flat.astype(np.float16)
-    weights_f32 = weights_f16.astype(np.float32)  # WGSL stores as f32 literals
-
-    # Format weights as WGSL array
-    weights_str = ",\n  ".join(
-        ", ".join(f"{w:.6f}" for w in weights_f32[i:i+8])
-        for i in range(0, len(weights_f32), 8)
-    )
-
-    radius = kernel_size // 2
-    if is_output_layer:
-        activation = "output[c] = clamp(sum, 0.0, 1.0);  // Output layer"
-    elif layer_idx == 0:
-        activation = "output[c] = clamp(sum, 0.0, 1.0);  // Layer 0: clamp [0,1]"
-    else:
-        activation = "output[c] = max(0.0, sum);  // Middle layers: ReLU"
-
-    shader_code = f"""// CNN v2 Layer {layer_idx} - Auto-generated (uniform 12D→4D)
-// Kernel: {kernel_size}×{kernel_size}, In: 12D (4 prev + 8 static), Out: 4D
-// Mip level: {mip_level} (p0-p3 features)
-
-const KERNEL_SIZE: u32 = {kernel_size}u;
-const IN_CHANNELS: u32 = 12u;  // 4 (input/prev) + 8 (static)
-const OUT_CHANNELS: u32 = 4u;   // Uniform output
-const KERNEL_RADIUS: i32 = {radius};
-
-// Weights quantized to float16 (stored as f32 in WGSL)
-const weights: array<f32, {len(weights_f32)}> = array(
-  {weights_str}
-);
-
-@group(0) @binding(0) var static_features: texture_2d<u32>;
-@group(0) @binding(1) var layer_input: texture_2d<u32>;
-@group(0) @binding(2) var output_tex: texture_storage_2d<rgba32uint, write>;
-
-fn unpack_static_features(coord: vec2<i32>) -> array<f32, 8> {{
-  let packed = textureLoad(static_features, coord, 0);
-  let v0 = unpack2x16float(packed.x);
-  let v1 = unpack2x16float(packed.y);
-  let v2 = unpack2x16float(packed.z);
-  let v3 = unpack2x16float(packed.w);
-  return array<f32, 8>(v0.x, v0.y, v1.x, v1.y, v2.x, v2.y, v3.x, v3.y);
-}}
-
-fn unpack_layer_channels(coord: vec2<i32>) -> vec4<f32> {{
-  let packed = textureLoad(layer_input, coord, 0);
-  let v0 = unpack2x16float(packed.x);
-  let v1 = unpack2x16float(packed.y);
-  return vec4<f32>(v0.x, v0.y, v1.x, v1.y);
-}}
-
-fn pack_channels(values: vec4<f32>) -> vec4<u32> {{
-  return vec4<u32>(
-    pack2x16float(vec2<f32>(values.x, values.y)),
-    pack2x16float(vec2<f32>(values.z, values.w)),
-    0u,  // Unused
-    0u   // Unused
-  );
-}}
-
-@compute @workgroup_size(8, 8)
-fn main(@builtin(global_invocation_id) id: vec3<u32>) {{
-  let coord = vec2<i32>(id.xy);
-  let dims = textureDimensions(static_features);
-
-  if (coord.x >= i32(dims.x) || coord.y >= i32(dims.y)) {{
-    return;
-  }}
-
-  // Load static features (always available)
-  let static_feat = unpack_static_features(coord);
-
-  // Convolution: 12D input (4 prev + 8 static) → 4D output
-  var output: vec4<f32> = vec4<f32>(0.0);
-  for (var c: u32 = 0u; c < 4u; c++) {{
-    var sum: f32 = 0.0;
-
-    for (var ky: i32 = -KERNEL_RADIUS; ky <= KERNEL_RADIUS; ky++) {{
-      for (var kx: i32 = -KERNEL_RADIUS; kx <= KERNEL_RADIUS; kx++) {{
-        let sample_coord = coord + vec2<i32>(kx, ky);
-
-        // Border handling (clamp)
-        let clamped = vec2<i32>(
-          clamp(sample_coord.x, 0, i32(dims.x) - 1),
-          clamp(sample_coord.y, 0, i32(dims.y) - 1)
-        );
-
-        // Load features at this spatial location
-        let static_local = unpack_static_features(clamped);
-        let layer_local = unpack_layer_channels(clamped);  // 4D
-
-        // Weight index calculation
-        let ky_idx = u32(ky + KERNEL_RADIUS);
-        let kx_idx = u32(kx + KERNEL_RADIUS);
-        let spatial_idx = ky_idx * KERNEL_SIZE + kx_idx;
-
-        // Accumulate: previous/input channels (4D)
-        for (var i: u32 = 0u; i < 4u; i++) {{
-          let w_idx = c * 12u * KERNEL_SIZE * KERNEL_SIZE +
-                     i * KERNEL_SIZE * KERNEL_SIZE + spatial_idx;
-          sum += weights[w_idx] * layer_local[i];
-        }}
-
-        // Accumulate: static features (8D)
-        for (var i: u32 = 0u; i < 8u; i++) {{
-          let w_idx = c * 12u * KERNEL_SIZE * KERNEL_SIZE +
-                     (4u + i) * KERNEL_SIZE * KERNEL_SIZE + spatial_idx;
-          sum += weights[w_idx] * static_local[i];
-        }}
-      }}
-    }}
-
-    {activation}
-  }}
-
-  // Pack and store
-  textureStore(output_tex, coord, pack_channels(output));
-}}
-"""
-
-    output_path = Path(output_dir) / "cnn_v2" / f"cnn_v2_layer_{layer_idx}.wgsl"
-    output_path.write_text(shader_code)
-    print(f"  → {output_path}")
-
-
-def export_checkpoint(checkpoint_path, output_dir):
-    """Export PyTorch checkpoint to WGSL shaders.
-
-    Args:
-        checkpoint_path: Path to .pth checkpoint
-        output_dir: Output directory for shaders
-    """
-    print(f"Loading checkpoint: {checkpoint_path}")
-    checkpoint = torch.load(checkpoint_path, map_location='cpu')
-
-    state_dict = checkpoint['model_state_dict']
-    config = checkpoint['config']
-
-    kernel_size = config.get('kernel_size', 3)
-    num_layers = config.get('num_layers', 3)
-    mip_level = config.get('mip_level', 0)
-
-    print(f"Configuration:")
-    print(f"  Kernel size: {kernel_size}×{kernel_size}")
-    print(f"  Layers: {num_layers}")
-    print(f"  Mip level: {mip_level} (p0-p3 features)")
-    print(f"  Architecture: uniform 12D→4D")
-
-    output_dir = Path(output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"\nExporting shaders to {output_dir}/")
-
-    # All layers uniform: 12D→4D
-    for i in range(num_layers):
-        layer_key = f'layers.{i}.weight'
-        if layer_key not in state_dict:
-            raise ValueError(f"Missing weights for layer {i}: {layer_key}")
-
-        layer_weights = state_dict[layer_key].detach().numpy()
-        is_output = (i == num_layers - 1)
-
-        export_layer_shader(
-            layer_idx=i,
-            weights=layer_weights,
-            kernel_size=kernel_size,
-            output_dir=output_dir,
-            mip_level=mip_level,
-            is_output_layer=is_output
-        )
-
-    print(f"\nExport complete! Generated {num_layers} shader files.")
-
-
-def main():
-    parser = argparse.ArgumentParser(description='Export CNN v2 checkpoint to WGSL shaders')
-    parser.add_argument('checkpoint', type=str, help='Path to checkpoint .pth file')
-    parser.add_argument('--output-dir', type=str, default='workspaces/main/shaders',
-                        help='Output directory for shaders')
-
-    args = parser.parse_args()
-    export_checkpoint(args.checkpoint, args.output_dir)
-
-
-if __name__ == '__main__':
-    main()