def prune_model_mixed(model):
model.cpu()
slim=pruning.Autoslim(model,inputs=torch.randn(1,3,32,32),compression_ratio=0.6)
# layer_compression_rate={1:0.125,7:0.125,13:0.125,56:0.625}
# layer_index_record=[]
# for key,value in slim.index_of_layer().items():
# layer_index_record.append(key)
# print(key,value)
# # 0.25 0.5 0.625 0.75 0.875
# for num in layer_index_record:
# if num not in layer_compression_rate:
# if num<18:
# layer_compression_rate[num]=0.5
# elif num<33:
# layer_compression_rate[num]=0.625
# elif num<48:
# layer_compression_rate[num]=0.75
# else:
# layer_compression_rate[num]=0.875
#全自动化压缩+剪跳连层=分级剪枝
slim.fpgm_pruning()
#print('layer_compression_rate:\n',layer_compression_rate)
return model
def prune_model_mixed(model):
model.cpu()
slim = pruning.Autoslim(model,
inputs=torch.randn(1, 3, 32, 32),
compression_ratio=0.5)
config = {
'layer_compression_ratio': None,
'norm_rate': 1.0,
'prune_shortcut': 1,
'global_pruning': False,
'pruning_func': 'l1'
}
slim = pruning.Autoslim(model,
inputs=torch.randn(1, 3, 32, 32),
compression_ratio=0.5)
slim.base_prunging(config)
return model
def prune_model_with_shortcut(model):
model.cpu()
print(model)
slim=pruning.Autoslim(model,inputs=torch.randn(1,3,32,32),compression_ratio=0.795)
slim.l1_norm_pruning()
print('---------------------')
print(model)
return model
def prune_model_without_shortcut(model):
model.cpu()
slim=pruning.Autoslim(model,inputs=torch.randn(1,3,32,32),compression_ratio=0)
#print(model)
layer_compression_rate={5:0.2,11:0.5,18:0.5,26:0.5,33:0.75,41:0.75,48:0.875,56:0.875}
slim.l1_norm_pruning(layer_compression_ratio=layer_compression_rate)
#print(model)
return model
def prune_model_with_shortcut(model):
model.cpu()
slim=pruning.Autoslim(model,inputs=torch.randn(1,3,32,32),compression_ratio=0.5)
slim.l1_norm_pruning()
# DG = tp.DependencyGraph().build_dependency( model, torch.randn(1, 3, 32, 32) )
# def prune_conv(conv, pruned_prob):
# weight = conv.weight.detach().cpu().numpy()
# out_channels = weight.shape[0]
# L1_norm = np.sum( np.abs(weight), axis=(1,2,3))
# num_pruned = int(out_channels * pruned_prob)
# prune_index = np.argsort(L1_norm)[:num_pruned].tolist() # remove filters with small L1-Norm
# plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index)
# plan.exec()
# block_prune_probs = [0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3]
# blk_id = 0
# for m in model.modules():
# if isinstance( m, resnet.BasicBlock ):
# prune_conv( m.conv1, block_prune_probs[blk_id] )
# prune_conv( m.conv2, block_prune_probs[blk_id] )
# blk_id+=1
return model
def prune_model_mixed(model):
model.cpu()
slim=pruning.Autoslim(model,inputs=torch.randn(1,3,32,32),compression_ratio=0)
layer_compression_rate={}
layer_index_record=[]
for key,value in slim.index_of_layer().items():
layer_index_record.append(key)
#print(key,value)
for num in layer_index_record:
if num<18:
layer_compression_rate[num]=0.5
elif num<33:
layer_compression_rate[num]=0.625
elif num<48:
layer_compression_rate[num]=0.75
else:
layer_compression_rate[num]=0.875
#slim.l1_norm_pruning(layer_compression_ratio=layer_compression_rate)
slim.l1_norm_pruning()
print('---------------------------')
return model
import torch_pruning as pruning
from torchvision.models import resnet18
import torch
# 模型建立
model = resnet18()
flops_raw, params_raw = pruning.get_model_complexity_info(
model, (3, 224, 224), as_strings=True, print_per_layer_stat=False)
print('-[INFO] before pruning flops: ' + flops_raw)
print('-[INFO] before pruning params: ' + params_raw)
# 选择裁剪方式
mod = 'fpgm'
# 剪枝引擎建立
slim = pruning.Autoslim(model, inputs=torch.randn(
1, 3, 224, 224), compression_ratio=0.5)
if mod == 'fpgm':
config = {
'layer_compression_ratio': None,
'norm_rate': 1.0, 'prune_shortcut': 1,
'dist_type': 'l1', 'pruning_func': 'fpgm'
}
elif mod == 'l1':
config = {
'layer_compression_ratio': None,
'norm_rate': 1.0, 'prune_shortcut': 1,
'global_pruning': False, 'pruning_func': 'l1'
}
slim.base_prunging(config)
flops_new, params_new = pruning.get_model_complexity_info(