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

own-solver/neurogolf_solver/solvers/tiling.py

@@ -0,0 +1,429 @@
+#!/usr/bin/env python3
+"""Tiling, upscaling, mirror, concat, and spatial gather solvers."""
+
+import numpy as np
+from onnx import helper
+from itertools import product as iproduct
+from ..onnx_helpers import mk, _make_int64_init, _build_pad_node
+from ..data_loader import get_exs, fixed_shapes
+from ..gather_helpers import _build_gather_model, _build_gather_model_with_const
+
+
+def s_tile(td):
+    """Tiling solver."""
+    exs = get_exs(td)
+    in_shapes = set(inp.shape for inp, _ in exs)
+    if len(in_shapes) != 1:
+        return None
+    IH, IW = in_shapes.pop()
+    tiles = set()
+    for inp, out in exs:
+        OH, OW = out.shape
+        if OH % IH or OW % IW:
+            return None
+        rH, rW = OH // IH, OW // IW
+        if rH < 1 or rW < 1 or (rH == 1 and rW == 1):
+            return None
+        tiles.add((rH, rW))
+    if len(tiles) != 1:
+        return None
+    rH, rW = tiles.pop()
+    OH, OW = IH * rH, IW * rW
+    if OH > 30 or OW > 30:
+        return None
+    for inp, out in exs:
+        if not np.array_equal(out, np.tile(inp, (rH, rW))):
+            return None
+    pad_h, pad_w = 30 - OH, 30 - OW
+    inits = [
+        _make_int64_init('st', [0, 0, 0, 0]),
+        _make_int64_init('en', [1, 10, IH, IW]),
+        _make_int64_init('rp', [1, 1, rH, rW]),
+    ]
+    nodes = [
+        helper.make_node('Slice', ['input', 'st', 'en'], ['cr']),
+        helper.make_node('Tile', ['cr', 'rp'], ['tl']),
+    ]
+    nodes.append(_build_pad_node('tl', 'output', pad_h, pad_w, inits))
+    return mk(nodes, inits)
+
+
+def s_upscale(td):
+    """Upscaling solver."""
+    exs = get_exs(td)
+    in_shapes = set(inp.shape for inp, _ in exs)
+    if len(in_shapes) != 1:
+        return None
+    IH, IW = in_shapes.pop()
+    scales = set()
+    for inp, out in exs:
+        OH, OW = out.shape
+        if OH % IH or OW % IW:
+            return None
+        sH, sW = OH // IH, OW // IW
+        if sH < 2 or sW < 2:
+            return None
+        scales.add((sH, sW))
+    if len(scales) != 1:
+        return None
+    sH, sW = scales.pop()
+    OH, OW = IH * sH, IW * sW
+    if OH > 30 or OW > 30:
+        return None
+    for inp, out in exs:
+        if not np.array_equal(out, np.repeat(np.repeat(inp, sH, 0), sW, 1)):
+            return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for r in range(OH):
+        for c in range(OW):
+            idx[r, c] = [r // sH, c // sW]
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_kronecker(td):
+    """Kronecker product solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if OH % IH != 0 or OW % IW != 0:
+        return None
+    sH, sW = OH // IH, OW // IW
+    if sH < 2 or sW < 2:
+        return None
+    if OH > 30 or OW > 30:
+        return None
+    for inp, out in exs:
+        if not np.array_equal(out, np.kron(inp, np.ones((sH, sW), dtype=np.int64))):
+            return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for r in range(OH):
+        for c in range(OW):
+            idx[r, c] = [r // sH, c // sW]
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_nonuniform_scale(td):
+    """Non-uniform scaling solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    for fh, fw in [(1, 2), (2, 1), (1, 3), (3, 1), (2, 3), (3, 2), (1, 4), (4, 1), (2, 4), (4, 2)]:
+        if OH != IH * fh or OW != IW * fw:
+            continue
+        if OH > 30 or OW > 30:
+            continue
+        if all(np.array_equal(np.repeat(np.repeat(inp, fh, 0), fw, 1), out) for inp, out in exs):
+            idx = np.zeros((OH, OW, 2), dtype=np.int64)
+            for r in range(OH):
+                for c in range(OW):
+                    idx[r, c] = [r // fh, c // fw]
+            return _build_gather_model(OH, OW, idx)
+    return None
+
+
+def s_diagonal_tile(td):
+    """Diagonal tiling solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if OH % IH != 0 or OW % IW != 0:
+        return None
+    rH, rW = OH // IH, OW // IW
+    if rH != rW or rH < 2:
+        return None
+    if OH > 30 or OW > 30:
+        return None
+    for inp, out in exs:
+        for bi in range(rH):
+            for bj in range(rW):
+                block = out[bi * IH:(bi + 1) * IH, bj * IW:(bj + 1) * IW]
+                if bi == bj:
+                    if not np.array_equal(block, inp):
+                        return None
+                else:
+                    if not np.all(block == 0):
+                        return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    cst = np.full((OH, OW), -1, dtype=np.int64)
+    for bi in range(rH):
+        for bj in range(rW):
+            for lr in range(IH):
+                for lc in range(IW):
+                    oi, oj = bi * IH + lr, bj * IW + lc
+                    if bi == bj:
+                        idx[oi, oj] = [lr, lc]
+                    else:
+                        idx[oi, oj] = [-1, -1]
+                        cst[oi, oj] = 0
+    return _build_gather_model_with_const(IH, IW, OH, OW, idx, cst)
+
+
+def s_mirror_h(td):
+    """Horizontal mirror solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if OH != IH or OW != 2 * IW:
+        return None
+    if OW > 30:
+        return None
+    for inp, out in exs:
+        if not np.array_equal(np.concatenate([inp, np.flip(inp, 1)], 1), out):
+            return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for r in range(OH):
+        for c in range(OW):
+            sc = c if c < IW else 2 * IW - 1 - c
+            idx[r, c] = [r, sc]
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_mirror_v(td):
+    """Vertical mirror solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if OW != IW or OH != 2 * IH:
+        return None
+    if OH > 30:
+        return None
+    for inp, out in exs:
+        if not np.array_equal(np.concatenate([inp, np.flip(inp, 0)], 0), out):
+            return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for r in range(OH):
+        for c in range(OW):
+            sr = r if r < IH else 2 * IH - 1 - r
+            idx[r, c] = [sr, c]
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_quad_mirror(td):
+    """Quad mirror solver."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if OH != 2 * IH or OW != 2 * IW:
+        return None
+    if OH > 30 or OW > 30:
+        return None
+    for inp, out in exs:
+        expected = np.block([[inp, np.flip(inp, 1)],
+                             [np.flip(inp, 0), np.flip(np.flip(inp, 0), 1)]])
+        if not np.array_equal(expected, out):
+            return None
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for r in range(OH):
+        for c in range(OW):
+            sr = r if r < IH else 2 * IH - 1 - r
+            sc = c if c < IW else 2 * IW - 1 - c
+            idx[r, c] = [sr, sc]
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_concat(td):
+    """Concatenation solver with transformations."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    transforms = [
+        ('id', lambda x: x), ('fliplr', lambda x: np.fliplr(x)),
+        ('flipud', lambda x: np.flipud(x)), ('rot180', lambda x: np.rot90(x, 2)),
+    ]
+    if OH == IH and OW % IW == 0 and OW > IW:
+        n = OW // IW
+        if 2 <= n <= 4:
+            for combo in iproduct(range(4), repeat=n):
+                if all(np.array_equal(out, np.concatenate([transforms[t][1](inp) for t in combo], axis=1))
+                       for inp, out in exs):
+                    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+                    for oi in range(OH):
+                        for oj in range(OW):
+                            bj = oj // IW
+                            lr, lc = oi, oj % IW
+                            t = transforms[combo[bj]][0]
+                            if t == 'id':
+                                sr, sc = lr, lc
+                            elif t == 'fliplr':
+                                sr, sc = lr, IW - 1 - lc
+                            elif t == 'flipud':
+                                sr, sc = IH - 1 - lr, lc
+                            elif t == 'rot180':
+                                sr, sc = IH - 1 - lr, IW - 1 - lc
+                            idx[oi, oj] = [sr, sc]
+                    return _build_gather_model(OH, OW, idx)
+    if OW == IW and OH % IH == 0 and OH > IH:
+        n = OH // IH
+        if 2 <= n <= 4:
+            for combo in iproduct(range(4), repeat=n):
+                if all(np.array_equal(out, np.concatenate([transforms[t][1](inp) for t in combo], axis=0))
+                       for inp, out in exs):
+                    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+                    for oi in range(OH):
+                        for oj in range(OW):
+                            bi = oi // IH
+                            lr, lc = oi % IH, oj
+                            t = transforms[combo[bi]][0]
+                            if t == 'id':
+                                sr, sc = lr, lc
+                            elif t == 'fliplr':
+                                sr, sc = lr, IW - 1 - lc
+                            elif t == 'flipud':
+                                sr, sc = IH - 1 - lr, lc
+                            elif t == 'rot180':
+                                sr, sc = IH - 1 - lr, IW - 1 - lc
+                            idx[oi, oj] = [sr, sc]
+                    return _build_gather_model(OH, OW, idx)
+    return None
+
+
+def s_concat_enhanced(td):
+    """Enhanced concatenation with all 8 dihedral transforms."""
+    exs = get_exs(td)
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    if IH == OH and IW == OW:
+        return None
+    if OH % IH != 0 or OW % IW != 0:
+        return None
+    rH, rW = OH // IH, OW // IW
+    if rH * rW > 16 or rH * rW < 2:
+        return None
+    if OH > 30 or OW > 30:
+        return None
+    transforms = [
+        ('id', lambda x: x), ('fliplr', lambda x: np.fliplr(x)),
+        ('flipud', lambda x: np.flipud(x)), ('rot180', lambda x: np.rot90(x, 2)),
+        ('rot90', lambda x: np.rot90(x, 1)), ('rot270', lambda x: np.rot90(x, 3)),
+        ('T', lambda x: x.T), ('T_fliplr', lambda x: np.fliplr(x.T)),
+    ]
+    block_transforms = {}
+    for bi in range(rH):
+        for bj in range(rW):
+            found = None
+            for tidx, (tname, tfn) in enumerate(transforms):
+                ok = True
+                for inp, out in exs:
+                    block = out[bi * IH:(bi + 1) * IH, bj * IW:(bj + 1) * IW]
+                    expected = tfn(inp)
+                    if expected.shape != (IH, IW) or not np.array_equal(block, expected):
+                        ok = False
+                        break
+                if ok:
+                    found = (tidx, tname)
+                    break
+            if found is None:
+                return None
+            block_transforms[(bi, bj)] = found
+    idx = np.zeros((OH, OW, 2), dtype=np.int64)
+    for bi in range(rH):
+        for bj in range(rW):
+            _, tname = block_transforms[(bi, bj)]
+            for lr in range(IH):
+                for lc in range(IW):
+                    oi, oj = bi * IH + lr, bj * IW + lc
+                    if tname == 'id':
+                        sr, sc = lr, lc
+                    elif tname == 'fliplr':
+                        sr, sc = lr, IW - 1 - lc
+                    elif tname == 'flipud':
+                        sr, sc = IH - 1 - lr, lc
+                    elif tname == 'rot180':
+                        sr, sc = IH - 1 - lr, IW - 1 - lc
+                    elif tname == 'rot90':
+                        sr, sc = IW - 1 - lc, lr
+                    elif tname == 'rot270':
+                        sr, sc = lc, IH - 1 - lr
+                    elif tname == 'T':
+                        sr, sc = lc, lr
+                    elif tname == 'T_fliplr':
+                        sr, sc = IW - 1 - lc, lr
+                    idx[oi, oj] = [sr, sc]
+    for inp, out in exs:
+        reconstructed = np.zeros_like(out)
+        for oi in range(OH):
+            for oj in range(OW):
+                reconstructed[oi, oj] = inp[idx[oi, oj, 0], idx[oi, oj, 1]]
+        if not np.array_equal(reconstructed, out):
+            return None
+    return _build_gather_model(OH, OW, idx)
+
+
+def s_spatial_gather(td):
+    """Spatial gather solver."""
+    sp = fixed_shapes(td)
+    if sp is None:
+        return None
+    (IH, IW), (OH, OW) = sp
+    exs = get_exs(td)
+    idx = np.full((OH, OW, 2), -1, dtype=np.int64)
+    cst = np.full((OH, OW), -1, dtype=np.int64)
+    for oi in range(OH):
+        for oj in range(OW):
+            vals = set(int(out[oi, oj]) for _, out in exs)
+            if len(vals) == 1:
+                cst[oi, oj] = vals.pop()
+            found = False
+            for ri in range(IH):
+                for rj in range(IW):
+                    if all(int(inp[ri, rj]) == int(out[oi, oj]) for inp, out in exs):
+                        idx[oi, oj] = [ri, rj]
+                        found = True
+                        break
+                if found:
+                    break
+            if not found and cst[oi, oj] < 0:
+                return None
+    return _build_gather_model_with_const(IH, IW, OH, OW, idx, cst)
+
+
+def s_varshape_spatial_gather(td):
+    """Variable shape spatial gather solver."""
+    sp = fixed_shapes(td)
+    if sp is not None:
+        return None
+    exs = get_exs(td)
+    exs_30 = []
+    for inp, out in exs:
+        ih, iw = inp.shape
+        oh, ow = out.shape
+        inp30 = np.zeros((30, 30), dtype=np.int64)
+        out30 = np.zeros((30, 30), dtype=np.int64)
+        inp30[:ih, :iw] = inp
+        out30[:oh, :ow] = out
+        exs_30.append((inp30, out30))
+    idx = np.full((30, 30, 2), -1, dtype=np.int64)
+    cst = np.full((30, 30), -1, dtype=np.int64)
+    for oi in range(30):
+        for oj in range(30):
+            vals = set(int(out30[oi, oj]) for _, out30 in exs_30)
+            if len(vals) == 1:
+                cst[oi, oj] = vals.pop()
+            found = False
+            for ri in range(30):
+                for rj in range(30):
+                    if all(int(inp30[ri, rj]) == int(out30[oi, oj]) for inp30, out30 in exs_30):
+                        idx[oi, oj] = [ri, rj]
+                        found = True
+                        break
+                if found:
+                    break
+            if not found and cst[oi, oj] < 0:
+                return None
+    return _build_gather_model_with_const(30, 30, 30, 30, idx, cst)