什么是去中心化存储？
去中心化存储是一种基于区块链去中心化网络的存储解决方案，而不是依赖于单一的中心化实体。数据保存在分散网络中的各个节点上，而不是保存在单一机构控制下的单一服务器上。
IPFS是一个去中心化的点对点文件存储网络，允许用户以分布式方式存储、访问和共享文件，提供更高的安全性、隐私性和可扩展性。StorX使任何人都可以在全球多个托管节点之间安全地加密、分段和分发关键数据。存储在StorX上的每个文件在加密之前都被分成多个部分，并存储在由位于世界各地的不同运营商运行的独立存储节点中。
def fuse_bn(self):开发需求I35源码7O98设计O7I8
search_engine=SearchableGraph(graph=self.graph)
paths=search_engine.path_matching(
sp_expr=lambda x:x.type in{'Conv','Gemm','ConvTranspose'},
rp_expr=lambda x,y:False,
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ep_expr=lambda x:x.type=='BatchNormalization',
direction='down')
for path in paths:
path=path.tolist()
assert len(path)==2,('Oops seems we got something unexpected.')
computing_op,bn_op=path
assert isinstance(computing_op,Operation)and isinstance(bn_op,Operation)
if(len(self.graph.get_downstream_operations(computing_op))!=1 or
len(self.graph.get_upstream_operations(bn_op))!=1):
ppq_warning(f'PPQ can not merge operation{computing_op.name}and{bn_op.name},'
'this is not suppose to happen with your network,'
'network with batchnorm inside might not be able to quantize and deploy.')
continue
assert len(bn_op.parameters)==4,'BatchNorm should have 4 parameters,namely alpha,beta,mean,var'
alpha=bn_op.parameters[0].value
beta=bn_op.parameters[1].value
mean=bn_op.parameters[2].value
var=bn_op.parameters[3].value
epsilon=bn_op.attributes.get('epsilon',1e-5)
if computing_op.num_of_parameter==1:
w=computing_op.parameters[0].value#no bias.
assert isinstance(w,torch.Tensor),'values of parameters are assumed as torch Tensor'
if computing_op.type=='ConvTranspose':
b=torch.zeros(w.shape[1]*computing_op.attributes.get('group',1))
elif computing_op.type=='Gemm'and computing_op.attributes.get('transB',0)==0:
b=torch.zeros(w.shape[1])
else:
b=torch.zeros(w.shape[0])
else:
w,b=[var.value for var in computing_op.parameters[:2]]#has bias.
if computing_op.type=='Conv':
#calculate new weight and bias
scale=alpha/torch.sqrt(var+epsilon)
w=wscale.reshape([-1]+[1](w.ndim-1))
b=alpha*(b-mean)/torch.sqrt(var+epsilon)+beta
elif computing_op.type=='Gemm':
#calculate new weight and bias
scale=alpha/torch.sqrt(var+epsilon)
if computing_op.attributes.get('transB',0):
w=w*scale.reshape([-1,1])
else:
w=w*scale.reshape([1,-1])
b=alpha*(b-mean)/torch.sqrt(var+epsilon)+beta
elif computing_op.type=='ConvTranspose':
scale=alpha/torch.sqrt(var+epsilon)
group=computing_op.attributes.get('group',1)
scale=scale.reshape([group,1,-1,1,1])
w=w.reshape([group,-1,w.shape[1],w.shape[2],w.shape[3]])*scale
w=w.reshape([w.shape[0]*w.shape[1],w.shape[2],w.shape[3],w.shape[4]])
b=alpha*(b-mean)/torch.sqrt(var+epsilon)+beta
else:
raise TypeError(
f'Unexpected op type{computing_op.type}.'
f'Can not merge{computing_op.name}with{bn_op.name}')
#create new op and variable
merged_op=Operation(computing_op.name,op_type=computing_op.type,
attributes=computing_op.attributes.copy())
weight_var=Variable(computing_op.name+'_weight',w,True,[merged_op])
bias_var=Variable(computing_op.name+'_bias',b,True,[merged_op])
#replace&dirty work
input_var=computing_op.inputs[0]
output_var=bn_op.outputs[0]
input_var.dest_ops.remove(computing_op)
input_var.dest_ops.append(merged_op)
output_var.source_op=merged_op
#delete old operations
computing_op.inputs.pop(0)
bn_op.outputs.clear()
self.graph.remove_operation(computing_op)
#insert new
self.graph.append_operation(merged_op)
merged_op.inputs.extend([input_var,weight_var,bias_var])
merged_op.outputs.extend([output_var])
self.graph.append_variable(weight_var)
self.graph.append_variable(bias_var)
