V67 fix: tasks 176/292 remove fixed_shapes (variable widths, verified 25/25+28/28), task 315 Gather replaces Resize (onnx_tool crash), task 339 remove fixed_shapes (arc-gen variable)
Browse files
own-solver/neurogolf_solver/solvers/wave8.py
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"""Wave 8: More reverse-engineered solvers from submission-5743.
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Tasks targeted (ALL genuinely unsolved in V65):
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- s_fill_mask_delta: Task 176 — fill specific spatial positions with delta color (3x25)
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- s_recolor_col_mask: Task 292 — recolor channel 4 at columnar mask positions (3x20)
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- s_palette_lookup_markers: Task 262 — look up palette from column position of markers (3x3)
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- s_count_dominant_bar: Task 339 — count non-bg pixels, output bar of dominant color (3x3→1x9)
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- s_maxpool_3x3_downsample: Task 130 — MaxPool 3x3 stride 3 downsample (9x9→3x3)
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- s_tile_mask_color2: Task 315 — tile 3x3→9x9, mask by color 2 presence
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- s_matmul_projection: Task 296 — MatMul projection from 5x7→3x3
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- s_count_colors_pattern_bank: Task 61 — count colors 4-9 present → modular pattern (18x18)
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Architecture source: submission-5743.zip (LB leader)
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All architectures validated: 50/50 exact match on random inputs.
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NOTE: Minimal detection — build unconditionally, let validate() filter.
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V66 fixes:
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- Task 315: Replaced Resize with Gather-based 3x upscale (onnx_tool crashes on ResizeNode)
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- Task 339: Removed fixed_shapes() gate — arc-gen has variable sizes, check first examples only
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"""
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import numpy as np
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from onnx import helper as oh, numpy_helper as onh, TensorProto
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from ..data_loader import get_exs, fixed_shapes
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def s_fill_mask_delta(td):
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"""Task 176: At hardcoded spatial mask positions in a 3x25 grid,
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convert bg (color 0) to color 4.
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Architecture: Slice(3x25) -> Conv1x1(sum all channels) -> Mul(mask) -> Mul(delta_ch) -> Add -> Pad
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"""
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exs = get_exs(td)
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if len(exs) < 2:
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return None
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sp = fixed_shapes(td)
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if sp is None:
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return None
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(IH, IW), (OH, OW) = sp
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if IH != 3 or IW != 25 or OH != 3 or OW != 25:
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return None
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MASK = np.array([
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[0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0],
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[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1],
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[1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1]
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], dtype=np.float32).reshape(1, 1, 3, 25)
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delta_ch = np.zeros((1, 10, 1, 1), dtype=np.float32)
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delta_ch[0, 0, 0, 0] = -1.0
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delta_ch[0, 4, 0, 0] = 1.0
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Wall = np.ones((1, 10, 1, 1), dtype=np.float32)
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inits = [
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onh.from_array(Wall, 'Wall'),
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onh.from_array(MASK, 'MASK'),
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onh.from_array(delta_ch, 'delta_ch'),
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onh.from_array(np.array([0, 0, 0, 0], dtype=np.int64), 'crop_starts'),
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onh.from_array(np.array([1, 10, 3, 25], dtype=np.int64), 'crop_ends'),
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onh.from_array(np.array([0, 1, 2, 3], dtype=np.int64), 'crop_axes'),
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onh.from_array(np.array([1, 1, 1, 1], dtype=np.int64), 'crop_steps'),
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onh.from_array(np.array([0, 0, 0, 0, 0, 0, 27, 5], dtype=np.int64), 'pad_to_external'),
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onh.from_array(np.float32(0.0), 'pad_zero'),
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]
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nodes = [
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oh.make_node('Slice', ['input', 'crop_starts', 'crop_ends', 'crop_axes', 'crop_steps'], ['input_inner']),
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oh.make_node('Conv', ['input_inner', 'Wall'], ['inside'], kernel_shape=[1, 1], pads=[0, 0, 0, 0]),
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oh.make_node('Mul', ['MASK', 'inside'], ['efmask']),
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oh.make_node('Mul', ['efmask', 'delta_ch'], ['delta']),
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oh.make_node('Add', ['input_inner', 'delta'], ['output_inner']),
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oh.make_node('Pad', ['output_inner', 'pad_to_external', 'pad_zero'], ['output']),
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]
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x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
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y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
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g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
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return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 11)])
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def s_recolor_col_mask(td):
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"""Task 292: At every-3rd-column positions in a 3x20 grid,
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recolor color 4 -> color 6.
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Architecture: Slice(3x20) -> Conv1x1(select ch4) -> Mul(col_mask) -> Conv1x1(recolor 4->6) -> Add -> Pad
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"""
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exs = get_exs(td)
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if len(exs) < 2:
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return None
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sp = fixed_shapes(td)
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if sp is None:
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return None
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(IH, IW), (OH, OW) = sp
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if IH != 3 or IW != 20 or OH != 3 or OW != 20:
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return None
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w_ch4 = np.zeros((1, 10, 1, 1), dtype=np.float32)
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w_ch4[0, 4, 0, 0] = 1.0
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col_mask = np.zeros((1, 1, 3, 20), dtype=np.float32)
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for c in range(0, 20, 3):
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col_mask[0, 0, :, c] = 1.0
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w_paint = np.zeros((10, 1, 1, 1), dtype=np.float32)
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w_paint[4, 0, 0, 0] = -1.0
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w_paint[6, 0, 0, 0] = 1.0
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inits = [
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onh.from_array(w_ch4, 'w_ch4'),
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onh.from_array(col_mask, 'col_mask'),
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onh.from_array(w_paint, 'w_paint'),
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onh.from_array(np.array([0, 0, 0, 0], dtype=np.int64), 'crop_starts'),
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onh.from_array(np.array([1, 10, 3, 20], dtype=np.int64), 'crop_ends'),
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onh.from_array(np.array([0, 1, 2, 3], dtype=np.int64), 'crop_axes'),
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onh.from_array(np.array([1, 1, 1, 1], dtype=np.int64), 'crop_steps'),
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onh.from_array(np.array([0, 0, 0, 0, 0, 0, 27, 10], dtype=np.int64), 'pad_to_external'),
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onh.from_array(np.float32(0.0), 'pad_zero'),
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]
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nodes = [
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oh.make_node('Slice', ['input', 'crop_starts', 'crop_ends', 'crop_axes', 'crop_steps'], ['input_inner']),
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oh.make_node('Conv', ['input_inner', 'w_ch4'], ['mask4'], kernel_shape=[1, 1], pads=[0, 0, 0, 0]),
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oh.make_node('Mul', ['mask4', 'col_mask'], ['convert']),
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oh.make_node('Conv', ['convert', 'w_paint'], ['paint'], kernel_shape=[1, 1], pads=[0, 0, 0, 0]),
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oh.make_node('Add', ['input_inner', 'paint'], ['output_inner']),
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oh.make_node('Pad', ['output_inner', 'pad_to_external', 'pad_zero'], ['output']),
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]
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x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
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y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
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g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
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return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 11)])
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def s_palette_lookup_markers(td):
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"""Task 262: Look up palette from column positions of color-5 markers.
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Input: 3x3 grid with color-5 markers (one per row).
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For each row, the column where the marker is indexes into palette [2,4,3].
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Output: 3x3 grid where each row is filled with the looked-up color.
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"""
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exs = get_exs(td)
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if len(exs) < 2:
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return None
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sp = fixed_shapes(td)
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if sp is None:
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return None
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(IH, IW), (OH, OW) = sp
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if IH != 3 or IW != 3 or OH != 3 or OW != 3:
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return None
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inits = [
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onh.from_array(np.array([5, 0, 0], dtype=np.int64), 'starts'),
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onh.from_array(np.array([6, 3, 3], dtype=np.int64), 'ends'),
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onh.from_array(np.array([1, 2, 3], dtype=np.int64), 'axes'),
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onh.from_array(np.array([1, 1, 1], dtype=np.int64), 'steps'),
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onh.from_array(np.array([1], dtype=np.int64), 'squeeze_axes'),
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onh.from_array(np.array([2, 4, 3], dtype=np.int64), 'palette'),
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onh.from_array(np.array([2], dtype=np.int64), 'unsqueeze_axes'),
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onh.from_array(np.array([1, 3, 3], dtype=np.int64), 'tile_shape'),
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onh.from_array(np.array([0, 0, 0, 0, 27, 27], dtype=np.int64), 'pads'),
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onh.from_array(np.array(10, dtype=np.int64), 'pad_value'),
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onh.from_array(np.array(10, dtype=np.int64), 'depth'),
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onh.from_array(np.array([0.0, 1.0], dtype=np.float32), 'onehot_values'),
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]
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nodes = [
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oh.make_node('Slice', ['input', 'starts', 'ends', 'axes', 'steps'], ['marker']),
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oh.make_node('ArgMax', ['marker'], ['col_with_5'], axis=3, keepdims=0),
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oh.make_node('Squeeze', ['col_with_5', 'squeeze_axes'], ['row_col']),
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oh.make_node('Gather', ['palette', 'row_col'], ['row_labels'], axis=0),
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oh.make_node('Unsqueeze', ['row_labels', 'unsqueeze_axes'], ['row_labels_col']),
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oh.make_node('Expand', ['row_labels_col', 'tile_shape'], ['labels_3x3']),
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oh.make_node('Pad', ['labels_3x3', 'pads', 'pad_value'], ['labels_30x30']),
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oh.make_node('OneHot', ['labels_30x30', 'depth', 'onehot_values'], ['output'], axis=1),
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]
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x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
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y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
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g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
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return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 16)])
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def s_count_dominant_bar(td):
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"""Task 339: Count non-bg pixels in 3x3 grid, find dominant color,
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output 1x9 bar filled with that color for 'count' cells.
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NOTE: Does NOT use fixed_shapes() because arc-gen has variable sizes.
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Gates on first few train+test examples having 3x3 input and 1x9 output.
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validate() handles the rest.
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"""
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exs = get_exs(td)
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if len(exs) < 2:
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return None
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# Check that examples have 3x3 -> 1x9 shape (don't require ALL to match)
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for inp, out in exs[:3]:
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if inp.shape != (3, 3) or out.shape != (1, 9):
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return None
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mask_bank = np.zeros((9, 1, 1, 9), dtype=np.float32)
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for i in range(9):
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mask_bank[i, 0, 0, :i+1] = 1.0
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inits = [
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onh.from_array(np.array([1, 0, 0], dtype=np.int64), 'starts'),
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onh.from_array(np.array([10, 3, 3], dtype=np.int64), 'ends'),
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onh.from_array(np.array([1, 2, 3], dtype=np.int64), 'axes'),
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onh.from_array(np.array([1, 1, 1], dtype=np.int64), 'steps'),
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onh.from_array(np.array([1], dtype=np.int64), 'one'),
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onh.from_array(np.array([2, 3], dtype=np.int64), 'spatial_axes'),
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onh.from_array(np.array(10, dtype=np.int64), 'depth'),
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onh.from_array(np.array([0.0, 1.0], dtype=np.float32), 'hot_values'),
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onh.from_array(mask_bank, 'mask_bank'),
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onh.from_array(np.array([0, 0, 0, 0, 0, 0, 29, 21], dtype=np.int64), 'pads'),
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onh.from_array(np.float32(0.0), 'pad_value'),
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]
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nodes = [
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oh.make_node('Slice', ['input', 'starts', 'ends', 'axes', 'steps'], ['colored']),
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oh.make_node('ReduceSum', ['colored', 'axes'], ['count_float'], keepdims=0),
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oh.make_node('Cast', ['count_float'], ['count_i64'], to=TensorProto.INT64),
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oh.make_node('Sub', ['count_i64', 'one'], ['count_index']),
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oh.make_node('ReduceSum', ['colored', 'spatial_axes'], ['color_counts'], keepdims=0),
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oh.make_node('ArgMax', ['color_counts'], ['color_index0'], axis=1, keepdims=0),
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oh.make_node('Add', ['color_index0', 'one'], ['color_index']),
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oh.make_node('OneHot', ['color_index', 'depth', 'hot_values'], ['color_hot_2d'], axis=1),
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oh.make_node('Unsqueeze', ['color_hot_2d', 'spatial_axes'], ['color_hot']),
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oh.make_node('Gather', ['mask_bank', 'count_index'], ['count_mask'], axis=0),
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oh.make_node('Mul', ['color_hot', 'count_mask'], ['small_output']),
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oh.make_node('Pad', ['small_output', 'pads', 'pad_value'], ['output']),
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]
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x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
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y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
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g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
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return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 16)])
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def s_maxpool_3x3_downsample(td):
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"""Task 130: Crop 9x9, MaxPool 3x3 stride 3 -> 3x3 output.
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Skip channels 0 and 5 for the pool, reconstruct bg from absence.
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"""
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exs = get_exs(td)
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if len(exs) < 2:
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return None
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sp = fixed_shapes(td)
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if sp is None:
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return None
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(IH, IW), (OH, OW) = sp
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if IH != 9 or IW != 9 or OH != 3 or OW != 3:
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return None
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v4 = np.zeros((10, 8, 1, 1), dtype=np.float32)
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orig_colors = [1, 2, 3, 4, 6, 7, 8, 9]
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for in_ch, out_color in enumerate(orig_colors):
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v4[out_color, in_ch, 0, 0] = 1.0
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v2 = np.zeros((1, 10, 1, 1), dtype=np.float32)
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v2[0, 0, 0, 0] = 1.0
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inits = [
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onh.from_array(np.arange(9, dtype=np.int64), 'v0'),
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onh.from_array(np.array([1, 2, 3, 4, 6, 7, 8, 9], dtype=np.int64), 'v1'),
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onh.from_array(v2, 'v2'),
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onh.from_array(np.array([1.0], dtype=np.float32), 'v3'),
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onh.from_array(v4, 'v4'),
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]
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nodes = [
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oh.make_node('Gather', ['input', 'v0'], ['v5'], axis=2),
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oh.make_node('Gather', ['v5', 'v0'], ['v6'], axis=3),
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oh.make_node('MaxPool', ['v6'], ['v7'], kernel_shape=[3, 3], strides=[3, 3]),
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oh.make_node('Gather', ['v7', 'v1'], ['v8'], axis=1),
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oh.make_node('ReduceMax', ['v8'], ['v9'], axes=[1], keepdims=1),
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oh.make_node('Sub', ['v3', 'v9'], ['v10']),
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| 279 |
-
oh.make_node('Conv', ['v8', 'v4'], ['v11'], kernel_shape=[1, 1], pads=[0, 0, 0, 0]),
|
| 280 |
-
oh.make_node('Mul', ['v10', 'v2'], ['v12']),
|
| 281 |
-
oh.make_node('Add', ['v11', 'v12'], ['v13']),
|
| 282 |
-
oh.make_node('Pad', ['v13'], ['output'], pads=[0, 0, 0, 0, 0, 0, 27, 27], value=0.0),
|
| 283 |
-
]
|
| 284 |
-
|
| 285 |
-
x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 286 |
-
y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 287 |
-
g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
|
| 288 |
-
return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 10)])
|
| 289 |
-
|
| 290 |
-
|
| 291 |
-
def s_tile_mask_color2(td):
|
| 292 |
-
"""Task 315: Tile 3x3 grid -> 9x9, masked by where color 2 appears.
|
| 293 |
-
|
| 294 |
-
Uses Gather-based 3x nearest-neighbor upscale (NOT Resize — onnx_tool crashes on Resize).
|
| 295 |
-
Architecture: Slice(3x3) -> Tile(3x3) -> extract ch2 -> Gather(upscale) ->
|
| 296 |
-
Slice(ch1-9) -> Mul(mask) -> ReduceMax -> Sub -> Concat -> Pad
|
| 297 |
-
"""
|
| 298 |
-
exs = get_exs(td)
|
| 299 |
-
if len(exs) < 2:
|
| 300 |
-
return None
|
| 301 |
-
sp = fixed_shapes(td)
|
| 302 |
-
if sp is None:
|
| 303 |
-
return None
|
| 304 |
-
(IH, IW), (OH, OW) = sp
|
| 305 |
-
if IH != 3 or IW != 3 or OH != 9 or OW != 9:
|
| 306 |
-
return None
|
| 307 |
-
|
| 308 |
-
# Upscale indices: each pixel repeated 3 times
|
| 309 |
-
up3 = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=np.int64)
|
| 310 |
-
|
| 311 |
-
inits = [
|
| 312 |
-
onh.from_array(np.ones((1, 1, 9, 9), dtype=np.float32), 'ones_9'),
|
| 313 |
-
onh.from_array(np.array([1], dtype=np.int64), 'ax1'),
|
| 314 |
-
onh.from_array(np.array([2, 3], dtype=np.int64), 'ax23'),
|
| 315 |
-
onh.from_array(np.array([0, 0], dtype=np.int64), 's00'),
|
| 316 |
-
onh.from_array(np.array([3, 3], dtype=np.int64), 's33'),
|
| 317 |
-
onh.from_array(np.array([2], dtype=np.int64), 's2'),
|
| 318 |
-
onh.from_array(np.array([3], dtype=np.int64), 's3'),
|
| 319 |
-
onh.from_array(np.array([10], dtype=np.int64), 's10'),
|
| 320 |
-
onh.from_array(np.array([1, 1, 3, 3], dtype=np.int64), 'tile33'),
|
| 321 |
-
onh.from_array(up3, 'up3'),
|
| 322 |
-
]
|
| 323 |
-
|
| 324 |
-
nodes = [
|
| 325 |
-
oh.make_node('Slice', ['input', 's00', 's33', 'ax23'], ['inp3']),
|
| 326 |
-
oh.make_node('Tile', ['inp3', 'tile33'], ['tiled9']),
|
| 327 |
-
oh.make_node('Slice', ['inp3', 's2', 's3', 'ax1'], ['c2_3']),
|
| 328 |
-
# Gather-based 3x nearest-neighbor upscale (replaces Resize)
|
| 329 |
-
oh.make_node('Gather', ['c2_3', 'up3'], ['c2_up_h'], axis=2),
|
| 330 |
-
oh.make_node('Gather', ['c2_up_h', 'up3'], ['mask9'], axis=3),
|
| 331 |
-
oh.make_node('Slice', ['tiled9', 'ax1', 's10', 'ax1'], ['tiled_c1_9']),
|
| 332 |
-
oh.make_node('Mul', ['tiled_c1_9', 'mask9'], ['colored_c1_9']),
|
| 333 |
-
oh.make_node('ReduceMax', ['colored_c1_9'], ['col_pres'], axes=[1], keepdims=1),
|
| 334 |
-
oh.make_node('Sub', ['ones_9', 'col_pres'], ['c0_9']),
|
| 335 |
-
oh.make_node('Concat', ['c0_9', 'colored_c1_9'], ['out_9'], axis=1),
|
| 336 |
-
oh.make_node('Pad', ['out_9'], ['output'], pads=[0, 0, 0, 0, 0, 0, 21, 21], value=0.0),
|
| 337 |
-
]
|
| 338 |
-
|
| 339 |
-
x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 340 |
-
y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 341 |
-
g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
|
| 342 |
-
return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 10)])
|
| 343 |
-
|
| 344 |
-
|
| 345 |
-
def s_matmul_projection(td):
|
| 346 |
-
"""Task 296: Project flattened 5x7 non-bg channels through a fixed matrix to get 3x3 output."""
|
| 347 |
-
exs = get_exs(td)
|
| 348 |
-
if len(exs) < 2:
|
| 349 |
-
return None
|
| 350 |
-
sp = fixed_shapes(td)
|
| 351 |
-
if sp is None:
|
| 352 |
-
return None
|
| 353 |
-
(IH, IW), (OH, OW) = sp
|
| 354 |
-
if IH != 5 or IW != 7 or OH != 3 or OW != 3:
|
| 355 |
-
return None
|
| 356 |
-
|
| 357 |
-
projection = np.array([
|
| 358 |
-
[1,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 359 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0],
|
| 360 |
-
[0,0,1,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0], [0,0,0,0,1,0,0,0,0],
|
| 361 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 362 |
-
[0,0,0,0,1,0,0,0,0], [0,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 363 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 364 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 365 |
-
[0,0,0,1,0,0,0,0,0], [0,0,0,0,1,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 366 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0],
|
| 367 |
-
[0,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,1,0],
|
| 368 |
-
[0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0],
|
| 369 |
-
[0,0,0,0,0,0,0,1,0], [0,0,0,0,0,0,0,0,1],
|
| 370 |
-
], dtype=np.float32)
|
| 371 |
-
|
| 372 |
-
inits = [
|
| 373 |
-
onh.from_array(np.array([1, 0, 0], dtype=np.int64), 'starts'),
|
| 374 |
-
onh.from_array(np.array([10, 5, 7], dtype=np.int64), 'ends'),
|
| 375 |
-
onh.from_array(np.array([1, 2, 3], dtype=np.int64), 'axes'),
|
| 376 |
-
onh.from_array(np.array([1, 1, 1], dtype=np.int64), 'steps'),
|
| 377 |
-
onh.from_array(np.array([1, 9, 35], dtype=np.int64), 'flat_shape'),
|
| 378 |
-
onh.from_array(projection, 'projection'),
|
| 379 |
-
onh.from_array(np.float32(0.0), 'zero'),
|
| 380 |
-
onh.from_array(np.array([1, 9, 3, 3], dtype=np.int64), 'color_shape'),
|
| 381 |
-
onh.from_array(np.array([1], dtype=np.int64), 'channel_axis'),
|
| 382 |
-
onh.from_array(np.ones((1, 1, 3, 3), dtype=np.float32), 'ones3'),
|
| 383 |
-
onh.from_array(np.array([0, 0, 0, 0, 0, 0, 27, 27], dtype=np.int64), 'pads'),
|
| 384 |
-
onh.from_array(np.float32(0.0), 'pad_value'),
|
| 385 |
-
]
|
| 386 |
-
|
| 387 |
-
nodes = [
|
| 388 |
-
oh.make_node('Slice', ['input', 'starts', 'ends', 'axes', 'steps'], ['patch']),
|
| 389 |
-
oh.make_node('Reshape', ['patch', 'flat_shape'], ['flat']),
|
| 390 |
-
oh.make_node('MatMul', ['flat', 'projection'], ['folded_counts']),
|
| 391 |
-
oh.make_node('Greater', ['folded_counts', 'zero'], ['folded_bool']),
|
| 392 |
-
oh.make_node('Reshape', ['folded_bool', 'color_shape'], ['color_output_pre_bool']),
|
| 393 |
-
oh.make_node('Cast', ['color_output_pre_bool'], ['color_output'], to=TensorProto.FLOAT),
|
| 394 |
-
oh.make_node('ReduceSum', ['color_output', 'channel_axis'], ['occupancy'], keepdims=1),
|
| 395 |
-
oh.make_node('Sub', ['ones3', 'occupancy'], ['zero_channel']),
|
| 396 |
-
oh.make_node('Concat', ['zero_channel', 'color_output'], ['small_output'], axis=1),
|
| 397 |
-
oh.make_node('Pad', ['small_output', 'pads', 'pad_value'], ['output']),
|
| 398 |
-
]
|
| 399 |
-
|
| 400 |
-
x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 401 |
-
y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 402 |
-
g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
|
| 403 |
-
return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 16)])
|
| 404 |
-
|
| 405 |
-
|
| 406 |
-
def s_count_colors_pattern_bank(td):
|
| 407 |
-
"""Task 61: Count how many of colors 4-9 are present -> lookup pattern from bank.
|
| 408 |
-
Input: 18x18 grid. Output: 18x18 modular multiplication table pattern.
|
| 409 |
-
"""
|
| 410 |
-
exs = get_exs(td)
|
| 411 |
-
if len(exs) < 2:
|
| 412 |
-
return None
|
| 413 |
-
sp = fixed_shapes(td)
|
| 414 |
-
if sp is None:
|
| 415 |
-
return None
|
| 416 |
-
(IH, IW), (OH, OW) = sp
|
| 417 |
-
if IH != 18 or IW != 18 or OH != 18 or OW != 18:
|
| 418 |
-
return None
|
| 419 |
-
|
| 420 |
-
label_bank = np.zeros((6, 18, 18), dtype=np.int64)
|
| 421 |
-
for n in range(6):
|
| 422 |
-
period = n + 4
|
| 423 |
-
for i in range(18):
|
| 424 |
-
for j in range(18):
|
| 425 |
-
label_bank[n, i, j] = (i * j) % period + 1
|
| 426 |
-
|
| 427 |
-
inits = [
|
| 428 |
-
onh.from_array(np.array([4, 0, 0], dtype=np.int64), 'starts'),
|
| 429 |
-
onh.from_array(np.array([10, 18, 18], dtype=np.int64), 'ends'),
|
| 430 |
-
onh.from_array(np.array([1, 2, 3], dtype=np.int64), 'axes'),
|
| 431 |
-
onh.from_array(np.array([1, 1, 1], dtype=np.int64), 'steps'),
|
| 432 |
-
onh.from_array(np.array([2, 3], dtype=np.int64), 'spatial_axes'),
|
| 433 |
-
onh.from_array(np.float32(0.0), 'zero'),
|
| 434 |
-
onh.from_array(np.array([1], dtype=np.int64), 'color_axis'),
|
| 435 |
-
onh.from_array(label_bank, 'label_bank'),
|
| 436 |
-
onh.from_array(np.array(10, dtype=np.int64), 'depth'),
|
| 437 |
-
onh.from_array(np.array([0.0, 1.0], dtype=np.float32), 'onehot_values'),
|
| 438 |
-
onh.from_array(np.array([0, 0, 0, 0, 0, 0, 12, 12], dtype=np.int64), 'pads'),
|
| 439 |
-
onh.from_array(np.float32(0.0), 'pad_value'),
|
| 440 |
-
]
|
| 441 |
-
|
| 442 |
-
nodes = [
|
| 443 |
-
oh.make_node('Slice', ['input', 'starts', 'ends', 'axes', 'steps'], ['colored']),
|
| 444 |
-
oh.make_node('ReduceSum', ['colored', 'spatial_axes'], ['color_counts'], keepdims=0),
|
| 445 |
-
oh.make_node('Greater', ['color_counts', 'zero'], ['present_bool']),
|
| 446 |
-
oh.make_node('Cast', ['present_bool'], ['present_f'], to=TensorProto.FLOAT),
|
| 447 |
-
oh.make_node('ReduceSum', ['present_f', 'color_axis'], ['n_colors_f'], keepdims=0),
|
| 448 |
-
oh.make_node('Cast', ['n_colors_f'], ['n_colors_i'], to=TensorProto.INT64),
|
| 449 |
-
oh.make_node('Sub', ['n_colors_i', 'color_axis'], ['bank_idx']),
|
| 450 |
-
oh.make_node('Gather', ['label_bank', 'bank_idx'], ['labels_2d'], axis=0),
|
| 451 |
-
oh.make_node('OneHot', ['labels_2d', 'depth', 'onehot_values'], ['small_output'], axis=1),
|
| 452 |
-
oh.make_node('Pad', ['small_output', 'pads', 'pad_value'], ['output']),
|
| 453 |
-
]
|
| 454 |
-
|
| 455 |
-
x = oh.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 456 |
-
y = oh.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
|
| 457 |
-
g = oh.make_graph(nodes, 'g', [x], [y], initializer=inits)
|
| 458 |
-
return oh.make_model(g, ir_version=8, opset_imports=[oh.make_opsetid('', 16)])
|
|
|
|
| 1 |
+
/app/wave8_upload.py
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