def forward(self, s0, s1, drop_prob):
"""
:param s0:
:param s1:
:param drop_prob:
:return:
"""
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, drop_prob):
#s0, s1 = s1, cell(s0, s1, self.drop_path_prob=0.2)
#self.preprocess首先经过一个简单的卷积网络操作，得到s0，s1
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1] #存储之前的状态
#self._steps=4
for i in range(self._steps):
#初始状态,i=0时：h1=s0，h2=s1
h1 = states[self._indices[2 * i]] #[0,0,1,0]
h2 = states[self._indices[2 * i + 1]] #[1,1,0,2]
# 最后一个0，2，0状态的那个来构成跳跃连接
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
#进行dropout操作
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2 #每个中间状态由之前的两个操作加和而来
states += [s]
#将最后4个状态cat起来
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
out = torch.cat([states[i] for i in self._concat], dim=1)
if self.mask is None:
self._create_mask(s1, out)
out = self.activation(out)
self.act = out
return out
def forward(self, s0, s1, drop_prob):
res0 = s0
res1 = s1
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
if self.layer == 0:
return torch.cat([states[i] for i in self._concat], dim=1)
else:
if self.reduction:
states_out = torch.cat([states[i] for i in self._concat], dim=1)
states_out += self.preprocess_res(res1)
return states_out
else:
states_out = torch.cat([states[i] for i in self._concat], dim=1)
states_out += res1
return states_out
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
if self._weight == None:
s = h1 + h2
else:
s = self._weight[2 * i] * h1 + self._weight[2 * i + 1] * h2
states += [s]
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1) # N，C，H, W
def forward(self, s0, s1, drop_prob):
"""
:param s0:
:param s1:
:param drop_prob:
:return:
"""
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
# for each noce i, find which previous two node we
# connect to and corresponding ops for them
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
# aggregation of ops result is arithmatic sum
s = h1 + h2
states += [s]
# concatenate outputs of all node which becomes the result of the cell
# this makes it necessory that wxh is same for all outputs
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
"""
Iterates over the number of feedfoward steps
and returns the states h1 and h2, and applies the corresponding operations to the
computed states two
"""
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, drop_prob):
s0p = self.preprocess0(s0)
s1p = self.preprocess1(s1)
states = [s0p, s1p]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
if self.shrink_channel:
out = None
for s in states[-len(self._concat):]:
if out is None:
out = s
else:
out = out + s
else:
out = torch.cat([states[i] for i in self._concat], dim=1)
'''
print('shape info')
print('output', out.shape)
print('origin', s0.shape, s1.shape)
print('preproc', s0p.shape, s1p.shape)
print('normal', self.residual_norm(s0).shape, self.residual_norm(s1).shape)
if self.reduction:
print('reduce all', self.residual_reduce(s0).shape, self.residual_reduce(s1).shape)
elif self.reduction_prev:
print('reduce s0', self.residual_reduce(s0).shape)
print('end')
'''
if self.reduction:
out = out + self.residual_wei * self.residual_reduce(s0)
out = out + self.residual_wei * self.residual_reduce(s1)
elif self.reduction_prev:
if s1.shape[1] < out.shape[1]:
s1 = s1.repeat(1, out.shape[1] // s1.shape[1], 1, 1)
out = out + self.residual_wei * self.residual_reduce(s0)
out = out + self.residual_wei * self.residual_norm(s1)
else:
if s0.shape[1] < out.shape[1]:
s0 = s0.repeat(1, out.shape[1] // s0.shape[1], 1, 1)
out = out + self.residual_wei * self.residual_norm(s0)
out = out + self.residual_wei * self.residual_norm(s1)
#return torch.cat([states[i] for i in self._concat], dim=1)
return out
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
j = 0
for i in self._genotype_nodes:
h1 = states[i[0]]
h2 = states[i[1]]
op1 = self._ops[2 * j]
op2 = self._ops[2 * j + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
# if i==0:
# h1=states[0]
# h2=states[1]
# op1=self._ops[0]
# op2=self._ops[1]
# h1=op1(h1)
# h2=op2(h2)
# s = h1 + h2
# if i==1:
# h1 = states[0]
# h2 = states[1]
# h3 = states[2]
# op1 = self._ops[2]
# op2 = self._ops[3]
# op3 = self._ops[4]
# h1=op1(h1)
# h2=op2(h2)
# h3=op3(h3)
# s=h1+h2+h3
# if i==2:
# h1 = states[0]
# h2 = states[1]
# h3 = states[2]
# h4 = states[3]
# op1 = self._ops[5]
# op2 = self._ops[6]
# op3 = self._ops[7]
# op4 = self._ops[8]
# h1 = op1(h1)
# h2 = op2(h2)
# h3 = op3(h3)
# h4 = op4(h4)
# s=h1+h2+h3+h4
states += [s]
j = j + 1
# return torch.cat([states[i] for i in self._concat], dim=1)
return torch.cat([states[i + 2] for i in range(self._steps)], dim=1)
def forward(self, s0, s1, drop_prob=-1):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
X0 = self.ops1[0](s0)
X1 = self.ops1[1](s1)
if self.training and drop_prob > 0.:
X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
# X2 = self.ops2[0] (X0+X1)
X2 = self.ops2[0](s0)
X3 = self.ops2[1](s1)
if self.training and drop_prob > 0.:
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
return torch.cat([X0, X1, X2, X3], dim=1)
def forward(self, s0, s1, drop_path_prob, weights):
if self._use_ckpt:
s0 = cp.checkpoint(self.preprocess0, s0)
s1 = cp.checkpoint(self.preprocess1, s1)
else:
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
offset = 0
for i in range(self._steps):
s = 0
for j, h in enumerate(states):
op = self._ops[offset + j]
if self._use_ckpt:
h = cp.checkpoint(op, *[h, weights[offset + j]])
else:
h = op(h, weights[offset + j])
if self.training and drop_path_prob > 0.:
if not isinstance(op, Identity):
h = drop_path(h, drop_path_prob)
s += h
offset += len(states)
states.append(s)
return torch.cat(states[-4:], dim=1)
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = {}
states['0'] = s0
states['1'] = s1
# get all the operations in current intermediate node
for to_i, ops in self._ops.items():
h = []
for from_i, op_i in ops.items():
# each edge may no more than one operation
if from_i not in states:
#print('Exist the isolate node, which id is {}, we need ignore it!'.format(from_i))
continue
h += [
sum([
op(states[from_i]) for op in op_i if from_i in states
])
]
out = sum(h)
if self.training and drop_prob > 0:
out = drop_path(out, drop_prob)
states[to_i] = out
return torch.cat([v for v in states.values()][2:], dim=1)
def forward(self, s0, s1, drop_prob):
"""
:param s0:
:param s1:
:param drop_prob:
:return:
"""
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._first_layers[2 * i]
op2 = self._first_layers[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self._second_layers:
at1 = self._second_layers[2 * i]
at2 = self._second_layers[2 * i + 1]
h1 = at1(h1)
h2 = at2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity) and not isinstance(
at1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity) and not isinstance(
at2, Identity):
h2 = drop_path(h2, drop_prob)
else:
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
out = torch.cat([states[i] for i in self._concat], dim=1)
if self._bottleneck:
out = self._bottleneck(out)
return out
def forward(self, s0, s1, drop_prob):
res0 = s0
res1 = s1
if self.reduction:
s0 = self.preprocess0_red(res0)
s1 = self.preprocess1_red(res1)
else:
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
if self.layer == 0:
return torch.cat([states[i] for i in self._concat], dim=1)
else:
if self.reduction:
states_out = torch.cat([states[i] for i in self._concat],
dim=1)
if res1.shape[2] % 2 == 1 and res1.shape[3] % 2 == 0:
preprocess_out = self.preprocess_res_xpad(res1)
elif res1.shape[2] % 2 == 0 and res1.shape[3] % 2 == 1:
preprocess_out = self.preprocess_res_ypad(res1)
else:
preprocess_out = self.preprocess_res(res1)
states_out += preprocess_out
return states_out
else:
# if self.layer>0:
states_out = torch.cat([states[i] for i in self._concat],
dim=1)
# states_out.append(states[1])
states_out += res1
return states_out
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2*i]]
h2 = states[self._indices[2*i+1]]
op1 = self._ops[2*i]
op2 = self._ops[2*i+1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not op1.is_identity_op():
h1 = drop_path(h1, drop_prob)
if not op2.is_identity_op():
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, x, drop_prob):
s0 = s1 = x
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
mask = self.activ(sum(states[2:]))
return x * mask
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps): # 遍历每个节点，建立连接
h1 = states[self._indices[2*i]] # 2*i取i个节点的第1个连接，指示i节点与之前第几个节点输出的feature map连接，h1表示建立连接后的要被处理的输入节点的特征向量
h2 = states[self._indices[2*i+1]]
op1 = self._ops[2*i]
op2 = self._ops[2*i+1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity): # 非恒等映射，dropout
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2 # 两个算子是按位加
states += [s] #存入states
return torch.cat([states[i] for i in self._concat], dim=1) # 取要保留的feature拼接
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
# I assume these two are the incoming operations of the complete cell
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
# compute sum and add it to the states
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, x, weights, drop_prob, eta_min, node_sum):
mix_op = 0
k = 0
for w, op in zip(weights, self._ops):
if w > eta_min * node_sum:
if not isinstance(op, Identity):
mix_op = mix_op + w * drop_path(op(x), drop_prob)
else:
mix_op = mix_op + w * op(x)
else:
if not isinstance(self._ops[k], Zero):
self._ops[k] = Zero(self.stride)
k += 1
return mix_op
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = {0: s0, 1: s1}
for op, f, t in zip(self._ops, self._f_nodes, self._t_nodes):
s = op(states[f])
if self.training and drop_prob > 0.:
if not isinstance(op, Identity):
s = drop_path(s, drop_prob)
if t in states:
states[t] = states[t] + s
else:
states[t] = s
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(4):
s = 0
for j in range(len(self._indices_output)):
if self._indices_output[j] == (i + 2):
h = states[self._indices_input[j]]
op = self._ops[j]
h = op(h)
if self.training and drop_prob > 0.:
if not isinstance(op, Identity):
h = drop_path(h, drop_prob)
s = s + h
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, weights, drop_prob=0.0):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
offset = 0
for i in range(self._steps):
if drop_prob > 0. and self.training:
s = sum(
drop_path(self._ops[offset + j](h, weights[offset +
j]), drop_prob)
for j, h in enumerate(states))
else:
s = sum(self._ops[offset + j](h, weights[offset + j])
for j, h in enumerate(states))
offset += len(states)
states.append(s)
return torch.cat(states[-self._multiplier:], dim=1)
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for edges in self.dag:
buffer = []
for op in edges:
if not isinstance(op, ops.Identity):
a = drop_path(op(states[op.s_idx]), drop_prob)
else:
a = op(states[op.s_idx])
buffer.append(a)
s_cur = sum(buffer)
# s_cur = sum(op(states[op.s_idx]) for op in edges)
states.append(s_cur)
s_out = torch.cat([states[i] for i in self.concat], dim=1)
return s_out
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
start = 0
end = start + 2
for i in range(self._steps):
s = 0
for j in range(start, end):
h = states[self._indices[j]]
op = self._ops[j]
h = op(h)
if self.training and drop_prob > 0.:
if not isinstance(op, Identity):
h = drop_path(h, drop_prob)
s += h
start = end
end += (i + 3)
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
