def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
for index, module in enumerate(self):
space = module.abstract_search_space
if not spaces.is_determined(space):
root_node.append(str(index), space)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
space_fc1 = self.fc1.abstract_search_space
space_fc2 = self.fc2.abstract_search_space
if not spaces.is_determined(space_fc1):
root_node.append("fc1", space_fc1)
if not spaces.is_determined(space_fc2):
root_node.append("fc2", space_fc2)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
for index, module in enumerate(self):
if not isinstance(module, SuperModule):
continue
space = module.abstract_search_space
if not spaces.is_determined(space):
root_node.append(str(index), space)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
space = self.proj.abstract_search_space
if not spaces.is_determined(space):
root_node.append("proj", space)
if not spaces.is_determined(self._embed_dim):
root_node.append("_embed_dim",
self._embed_dim.abstract(reuse_last=True))
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
if not spaces.is_determined(self._in_features):
root_node.append("_in_features",
self._in_features.abstract(reuse_last=True))
if not spaces.is_determined(self._out_features):
root_node.append("_out_features",
self._out_features.abstract(reuse_last=True))
if not spaces.is_determined(self._bias):
root_node.append("_bias", self._bias.abstract(reuse_last=True))
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
xdict = dict(
mha=self.mha.abstract_search_space,
norm1=self.norm1.abstract_search_space,
mlp=self.mlp.abstract_search_space,
norm2=self.norm2.abstract_search_space,
)
for key, space in xdict.items():
if not spaces.is_determined(space):
root_node.append(key, space)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
if not spaces.is_determined(self._embed_dim):
root_node.append("_embed_dim",
self._embed_dim.abstract(reuse_last=True))
xdict = dict(
input_embed=self.input_embed.abstract_search_space,
pos_embed=self.pos_embed.abstract_search_space,
backbone=self.backbone.abstract_search_space,
head=self.head.abstract_search_space,
)
for key, space in xdict.items():
if not spaces.is_determined(space):
root_node.append(key, space)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
space_q = self.q_fc.abstract_search_space
space_k = self.k_fc.abstract_search_space
space_v = self.v_fc.abstract_search_space
space_proj = self.proj.abstract_search_space
if not spaces.is_determined(self._num_heads):
root_node.append("_num_heads", self._num_heads.abstract(reuse_last=True))
if not spaces.is_determined(space_q):
root_node.append("q_fc", space_q)
if not spaces.is_determined(space_k):
root_node.append("k_fc", space_k)
if not spaces.is_determined(space_v):
root_node.append("v_fc", space_v)
if not spaces.is_determined(space_proj):
root_node.append("proj", space_proj)
return root_node
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
if not spaces.is_determined(self._d_model):
root_node.append("_d_model",
self._d_model.abstract(reuse_last=True))
return root_node
def abstract_search_space(self):
return spaces.VirtualNode(id(self))
