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))