Move own-solver/neurogolf_solver/solvers/mode.py to own-solver/

own-solver/neurogolf_solver/solvers/mode.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""Mode fill solver — output = solid fill of most common input color.
+
+v5.2: Solves Task 129 (score 19.451).
+Uses runtime ReduceSum→ArgMax→Expand for variable mode across inputs.
+Falls through to s_constant when mode is fixed across all examples.
+"""
+
+import numpy as np
+from onnx import helper, numpy_helper, TensorProto
+from ..onnx_helpers import mk, _make_int64_init, _build_pad_node
+from ..data_loader import get_exs, fixed_shapes
+from ..constants import GH, GW
+
+
+def s_mode_fill(td):
+    """Mode fill: output is entirely the most common color from input.
+    Uses runtime ArgMax to handle variable mode across inputs."""
+    exs = get_exs(td)
+
+    for inp, out in exs:
+        if inp.shape != out.shape:
+            return None
+        vals, counts = np.unique(inp, return_counts=True)
+        mode = vals[np.argmax(counts)]
+        if not np.all(out == mode):
+            return None
+
+    # Check if mode is always the same color
+    modes = set()
+    for inp, out in exs:
+        vals, counts = np.unique(inp, return_counts=True)
+        modes.add(vals[np.argmax(counts)])
+
+    if len(modes) == 1:
+        return None  # Let s_constant handle it
+
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if (IH, IW) != (OH, OW):
+        return None
+
+    pad_h, pad_w = GH - IH, GW - IW
+
+    inits = [
+        _make_int64_init('sl_st', [0, 0, 0, 0]),
+        _make_int64_init('sl_en', [1, 10, IH, IW]),
+        _make_int64_init('rs_axes_mode', [2, 3]),
+        numpy_helper.from_array(np.arange(10, dtype=np.int64).reshape(1, 10, 1, 1), 'classes'),
+    ]
+
+    nodes = [
+        helper.make_node('Slice', ['input', 'sl_st', 'sl_en'], ['cropped']),
+        helper.make_node('ReduceSum', ['cropped', 'rs_axes_mode'], ['hist'], keepdims=1),
+        helper.make_node('ArgMax', ['hist'], ['mode_idx'], axis=1, keepdims=1),
+        helper.make_node('Equal', ['mode_idx', 'classes'], ['eq']),
+        helper.make_node('Cast', ['eq'], ['mode_oh'], to=TensorProto.FLOAT),
+        helper.make_node('Expand', ['mode_oh', 'sl_en'], ['expanded']),
+    ]
+    nodes.append(_build_pad_node('expanded', 'output', pad_h, pad_w, inits))
+    return mk(nodes, inits)