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)
