| """Build optimized ONNX model for Task 084. |
| |
| Algorithm (175/175 verified): |
| 1. Input has a colored column at col 0 (N rows tall = grid is NxN) |
| 2. Draw anti-diagonal (color 2) from (0, N-1) to (N-2, 1) |
| 3. Fill bottom row (N-1, cols 1..N-1) with color 4 |
| 4. Keep original column 0 |
| |
| ONNX approach: |
| - Detect N from sum of non-bg channels |
| - Use row/col grids to compute masks for anti-diagonal and bottom row |
| - Combine into output using channel selection |
| |
| Score: 175/175 pass, file size 10KB (~12 pts estimated) |
| |
| Usage: |
| python build_task084_onnx.py --task-data-dir ../task-data |
| """ |
| from onnx import TensorProto |
| import numpy as np |
| from onnx_builder import OnnxBuilder, build_and_validate |
|
|
|
|
| def build_task084(): |
| b = OnnxBuilder() |
| const, nd = b.const, b.nd |
|
|
| const('c_one', [1.0]) |
| const('c_zero', [0.0]) |
| const('c_half', [0.5]) |
| const('axes123', [1, 2, 3], 'i') |
| const('axes23', [2, 3], 'i') |
| const('axes1', [1], 'i') |
| const('shape_1_1_30_30', [1, 1, 30, 30], 'i') |
| const('shape_1_1_1_1', [1, 1, 1, 1], 'i') |
| const('shape_1_10_1_1', [1, 10, 1, 1], 'i') |
| |
| row_vals = np.arange(30, dtype=np.float32).reshape(1, 1, 30, 1) * np.ones((1, 1, 1, 30), dtype=np.float32) |
| col_vals = np.arange(30, dtype=np.float32).reshape(1, 1, 1, 30) * np.ones((1, 1, 30, 1), dtype=np.float32) |
| const('row_grid', row_vals) |
| const('col_grid', col_vals) |
| |
| const('idx_0', [0], 'i') |
| const('idx_1', [1], 'i') |
| const('idx_2', [2], 'i') |
| const('idx_10', [10], 'i') |
| const('depth_10', [10.0]) |
| const('oh_vals', [0.0, 1.0]) |
| const('ch2_idx', [2], 'i') |
| const('ch4_idx', [4], 'i') |
|
|
| nonbg = nd('Slice', ['input', 'idx_1', 'idx_10', 'idx_1'], [[1, 9, 30, 30]]) |
| n_total = nd('ReduceSum', [nonbg, 'axes123'], [[1, 1, 1, 1]], keepdims=1) |
| |
| row_plus_col = nd('Add', ['row_grid', 'col_grid'], [[1, 1, 30, 30]]) |
| n_minus_1 = nd('Sub', [n_total, 'c_one'], [[1, 1, 1, 1]]) |
| |
| on_antidiag = nd('Equal', [row_plus_col, n_minus_1], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| col_gt_0 = nd('Greater', ['col_grid', 'c_zero'], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| row_lt_nm1 = nd('Less', ['row_grid', n_minus_1], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| ad_and_col = nd('And', [on_antidiag, col_gt_0], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| antidiag_mask = nd('And', [ad_and_col, row_lt_nm1], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| |
| on_bottom = nd('Equal', ['row_grid', n_minus_1], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| bottom_and_col = nd('And', [on_bottom, col_gt_0], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| col_lt_n_bool = nd('Less', ['col_grid', n_total], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| bottom_mask = nd('And', [bottom_and_col, col_lt_n_bool], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| |
| antidiag_f = nd('Cast', [antidiag_mask], [[1, 1, 30, 30]], to=1) |
| bottom_f = nd('Cast', [bottom_mask], [[1, 1, 30, 30]], to=1) |
| |
| col_eq_0 = nd('Equal', ['col_grid', 'c_zero'], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| col_eq_0_f = nd('Cast', [col_eq_0], [[1, 1, 30, 30]], to=1) |
| row_lt_n = nd('Less', ['row_grid', n_total], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| row_lt_n_f = nd('Cast', [row_lt_n], [[1, 1, 30, 30]], to=1) |
| col_lt_n = nd('Less', ['col_grid', n_total], [([1, 1, 30, 30], TensorProto.BOOL)]) |
| col_lt_n_f = nd('Cast', [col_lt_n], [[1, 1, 30, 30]], to=1) |
| inside_grid_f = nd('Mul', [row_lt_n_f, col_lt_n_f], [[1, 1, 30, 30]]) |
| col0_mask = nd('Mul', [col_eq_0_f, inside_grid_f], [[1, 1, 30, 30]]) |
| |
| col0_values = nd('Mul', ['input', col0_mask], [[1, 10, 30, 30]]) |
| |
| ch2_onehot = nd('OneHot', ['ch2_idx', 'depth_10', 'oh_vals'], [[1, 10]], axis=1) |
| ch2_onehot_4d = nd('Reshape', [ch2_onehot, 'shape_1_10_1_1'], [[1, 10, 1, 1]]) |
| antidiag_ch2 = nd('Mul', [ch2_onehot_4d, antidiag_f], [[1, 10, 30, 30]]) |
| |
| ch4_onehot = nd('OneHot', ['ch4_idx', 'depth_10', 'oh_vals'], [[1, 10]], axis=1) |
| ch4_onehot_4d = nd('Reshape', [ch4_onehot, 'shape_1_10_1_1'], [[1, 10, 1, 1]]) |
| bottom_ch4 = nd('Mul', [ch4_onehot_4d, bottom_f], [[1, 10, 30, 30]]) |
| |
| occupied_1 = nd('Add', [col0_mask, antidiag_f], [[1, 1, 30, 30]]) |
| occupied = nd('Add', [occupied_1, bottom_f], [[1, 1, 30, 30]]) |
| ch0_mask_raw = nd('Sub', [inside_grid_f, occupied], [[1, 1, 30, 30]]) |
| |
| const('ch0_idx', [0], 'i') |
| ch0_onehot = nd('OneHot', ['ch0_idx', 'depth_10', 'oh_vals'], [[1, 10]], axis=1) |
| ch0_onehot_4d = nd('Reshape', [ch0_onehot, 'shape_1_10_1_1'], [[1, 10, 1, 1]]) |
| bg_ch0 = nd('Mul', [ch0_onehot_4d, ch0_mask_raw], [[1, 10, 30, 30]]) |
| |
| out_1 = nd('Add', [col0_values, antidiag_ch2], [[1, 10, 30, 30]]) |
| out_2 = nd('Add', [out_1, bottom_ch4], [[1, 10, 30, 30]]) |
| final = nd('Add', [out_2, bg_ch0], [[1, 10, 30, 30]]) |
| |
| return b.finish('task084', last_tensor=final) |
|
|
|
|
| if __name__ == '__main__': |
| build_and_validate(build_task084, task_num=84) |
|
|
