1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
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__ = ['FPGMFilterPruner']
_logger = get_logger(__name__, logging.INFO)
class FPGMFilterPruner(FilterPruner):
def __init__(self, model, inputs, sen_file=None):
super(FPGMFilterPruner, self).__init__(model, inputs, sen_file=sen_file)
def cal_mask(self, var_name, pruned_ratio, group):
for _item in group[var_name]:
if _item['pruned_dims'] == [0]:
value = _item['value']
pruned_dims = _item['pruned_dims']
dist_sum_list = []
for out_i in range(value.shape[0]):
dist_sum = self.get_distance_sum(value, out_i)
dist_sum_list.append(dist_sum)
scores = np.array(dist_sum_list)
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
def get_distance_sum(self, value, out_idx):
w = value.view()
w.shape = value.shape[0], np.product(value.shape[1:])
selected_filter = np.tile(w[out_idx], (w.shape[0], 1))
x = w - selected_filter
x = np.sqrt(np.sum(x * x, -1))
return x.sum()
