v4.1: color_map via channel Gather for permutation maps (+15 pts), fix non-permutation fallback to Conv 1x1

neurogolf_solver.py

@@ -355,11 +355,26 @@ def s_color_map(td):
             iv, ov = int(iv), int(ov)
             if iv in cm and cm[iv] != ov: return None
             cm[iv] = ov
-    W = np.zeros((10,10,1,1), dtype=np.float32)
-    for ic in range(10):
-        W[cm.get(ic,ic), ic, 0, 0] = 1.0
-    return mk([helper.make_node('Conv', ['input','W'], ['output'], kernel_shape=[1,1])],
-              [numpy_helper.from_array(W, 'W')])
+    
+    # Check if it's a permutation (bijective + all mapped colors form a closed set)
+    is_permutation = (set(cm.keys()) == set(cm.values()))
+    
+    if is_permutation:
+        # Use channel Gather — zero MACs, much cheaper
+        gather_ch = np.arange(10, dtype=np.int32)
+        for src, dst in cm.items():
+            if 0 <= src < 10 and 0 <= dst < 10:
+                gather_ch[dst] = src
+        inits = [numpy_helper.from_array(gather_ch, 'gi')]
+        nodes = [helper.make_node('Gather', ['input', 'gi'], ['output'], axis=1)]
+        return mk(nodes, inits)
+    else:
+        # Non-permutation: use Conv 1x1 (has MACs but handles any mapping)
+        W = np.zeros((10,10,1,1), dtype=np.float32)
+        for ic in range(10):
+            W[cm.get(ic,ic), ic, 0, 0] = 1.0
+        return mk([helper.make_node('Conv', ['input','W'], ['output'], kernel_shape=[1,1])],
+                  [numpy_helper.from_array(W, 'W')])
 
 def s_transpose(td):
     for ex in td['train']+td['test']: