def object_2_attributes(vals): if isinstance(vals, dict): if 'type' in vals and 'par' in vals and len(vals) == 2: v1 = ModelParPair(vals['type']) update_config(v1, vals['par']) return v1 else: v2 = AttrDict() for key, value in vals.items(): v2[key] = object_2_attributes(value) return v2 elif isinstance(vals, list): v3 = [] for val in vals: v3.append(object_2_attributes(val)) return v3 else: return vals
__C.training_parameters.lambda_grl_steps = 0 __C.training_parameters.lambda_grl_start = 0 __C.training_parameters.lambda_q = 1.0 __C.training_parameters.static_lr = True # --------------------------------------------------------------------------- # # loss options: # --------------------------------------------------------------------------- # __C.loss = 'logitBCE' # --------------------------------------------------------------------------- # # optimizer options: # --------------------------------------------------------------------------- # __C.optimizer = ModelParPair('Adamax') # --------------------------------------------------------------------------- # # adv_optimizer options: # --------------------------------------------------------------------------- # __C.adv_optimizer = ModelParPair('adv_opt') # --------------------------------------------------------------------------- # # model options: Note default is our # --------------------------------------------------------------------------- # __C.model = AttrDict() __C.model.image_feat_dim = 2048 __C.model.question_embedding = [ModelParPair("att_que_embed")] __C.model.image_feature_encoding = [ModelParPair('default_image')]
__C.training_parameters.wu_iters = 1000 __C.training_parameters.max_iter = 12000 __C.training_parameters.lr_steps = [5000, 7000, 9000, 11000] __C.training_parameters.lr_ratio = 0.1 # --------------------------------------------------------------------------- # # loss options: # --------------------------------------------------------------------------- # __C.loss = "logitBCE" # --------------------------------------------------------------------------- # # optimizer options: # --------------------------------------------------------------------------- # __C.optimizer = ModelParPair("Adamax") # --------------------------------------------------------------------------- # # model options: Note default is our # --------------------------------------------------------------------------- # __C.model = AttrDict() __C.model.image_feat_dim = 2048 __C.model.question_embedding = [ModelParPair("att_que_embed")] __C.model.image_feature_encoding = [ModelParPair("default_image")] __C.model.image_embedding_models = [] __C.model.modal_combine = ModelParPair("non_linear_elmt_multiply") __C.model.classifier = ModelParPair("logit_classifier") top_down_bottom_up = AttrDict() top_down_bottom_up.modal_combine = ModelParPair("non_linear_elmt_multiply")