def apply_combination(vpr_args, io_grid, grid1, grid2, seed=None): with Silence(): vpr_context = VPRContext(vpr_args) p1 = vpr_context.get_random_placement() p1.set_io(io_grid) p1.set_grid(grid1) p2 = p1.copy() p2.set_grid(grid2) c = ConstrainedSwapPlacementCombination(p1, p2) best_grid, min_id, costs = c.swaps_min_placement(seed=None) return best_grid, min_id, costs
return args if __name__ == '__main__': import sys args = _parse_args() vpr_context = VPRContext.annealer_context(args.netlist_file, args.arch_file, args.seed) # Note that each repeated call to get_random_placement() will return a # unique placement. placement1 = vpr_context.get_random_placement() placement2 = vpr_context.get_random_placement() # Since we currently do not process I/O placement in the anneal, for our # comparison between placements, we want to start all placements with the # same I/O positions. Therefore, we will use the positions from placement1 # to apply to placement2. placement2.set_io(placement1.get_io_grid()) c = ConstrainedSwapPlacementCombination(placement1, placement2) # Perform all swaps between placement1 and placement2, recording the fitness # of each placement along the way. Note that the 'seed' parameter below may # be used to generate different orderings of swaps between placement1 and # placement2. best_grid, min_id, costs = c.swaps_min_placement(seed=None)