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import logging
import numpy as np
import paddle
from paddleslim.common import get_logger
from .var_group import *
from .pruning_plan import *
from .filter_pruner import FilterPruner
__all__ = ['L2NormFilterPruner']
_logger = get_logger(__name__, logging.INFO)
class L2NormFilterPruner(FilterPruner):
def __init__(self, model, inputs, sen_file=None):
super(L2NormFilterPruner, self).__init__(
model, inputs, sen_file=sen_file)
def cal_mask(self, var_name, pruned_ratio, group):
# find information of pruning on output channels
for _item in group[var_name]:
if _item['pruned_dims'] == [0]:
value = _item['value']
pruned_dims = _item['pruned_dims']
reduce_dims = [
i for i in range(len(value.shape)) if i not in pruned_dims
]
# scores = np.mean(np.abs(value), axis=tuple(reduce_dims))
scores = np.sqrt(np.sum(np.square(value), axis=tuple(reduce_dims)))
sorted_idx = scores.argsort()
pruned_num = int(round(len(sorted_idx) * pruned_ratio))
pruned_idx = sorted_idx[:pruned_num]
mask_shape = [value.shape[i] for i in pruned_dims]
mask = np.ones(mask_shape, dtype="int32")
mask[pruned_idx] = 0
return mask
