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299 def load_state_dict(self, state_dict): 300 self.vis_net.load_state_dict(state_dict[0]) 301 self.txt_net.load_state_dict(state_dict[1])
635 def set_state_dict(model: torch.nn.Module, state_dict: dict): 636 """Set state dict of a model. 637 638 Also works with ``torch.nn.DataParallel`` models.""" 639 try: 640 model.load_state_dict(state_dict) 641 # If self.model was saved as nn.DataParallel then remove 'module.' prefix 642 # in every key 643 except RuntimeError: # TODO: Is it safe to catch all runtime errors here? 644 new_state_dict = OrderedDict() 645 for k, v in state_dict.items(): 646 new_state_dict[k.replace('module.', '')] = v 647 model.load_state_dict(new_state_dict)
152 def load_state_dict(model: torch.nn.Module, state_dict: Dict, skip_wrong_shape: bool = False): 153 model_state_dict = model.state_dict() 154 155 for key in state_dict: 156 if key in model_state_dict: 157 if model_state_dict[key].shape == state_dict[key].shape: 158 model_state_dict[key] = state_dict[key] 159 elif not skip_wrong_shape: 160 m = ( 161 f"Shapes of the '{key}' parameters do not match: " 162 f"{model_state_dict[key].shape} vs {state_dict[key].shape}" 163 ) 164 raise Exception(m) 165 166 model.load_state_dict(model_state_dict)
60 def load_state_dict(self, state): 61 for key, val in state.items(): 62 self.__dict__[key] = val
85 def load_state_dict(self, state): 86 """Load model state 87 """ 88 # load network architecture and params 89 self.network_type = state['network_type'] 90 self.board_size = state['board_size'] 91 self.num_blocks = state['num_blocks'] 92 self.base_chans = state['base_chans'] 93 self.net = create_network(self.network_type, 94 self.board_size, 95 self.num_blocks, 96 self.base_chans) 97 self.net.load_state_dict(state['net']) 98 99 # load search params 100 self.simulations = state['simulations'] 101 self.search_batch_size = state['search_batch_size'] 102 self.exploration_coef = state['exploration_coef'] 103 self.exploration_depth = state['exploration_depth'] 104 self.exploration_noise_alpha = state['exploration_noise_alpha'] 105 self.exploration_noise_scale = state['exploration_noise_scale'] 106 self.exploration_temperature = state['exploration_temperature'] 107 108 # load random number generator state 109 if 'rng' in state: 110 self.rng.__setstate__(state['rng'])
127 def load_my_state_dict(self, state_dict, seq_len): 128 own_state = self.state_dict() 129 #print(own_state.keys()) 130 #pdb.set_trace() 131 for name, param in state_dict.items(): 132 if name in own_state.keys(): 133 if isinstance(param, Parameter): 134 # backwards compatibility for serialized parameters 135 param = param.data 136 137 if name.find('Conv2d_1a_3x3') > -1 and not name.find('bn') > -1: 138 param = param.repeat(1, seq_len, 1, 1) 139 param = param / float(seq_len) 140 141 try: 142 own_state[name].copy_(param) 143 except Exception: 144 raise RuntimeError('While copying the parameter named {}, ' 145 'whose dimensions in the model are {} and ' 146 'whose dimensions in the checkpoint are {}.' 147 .format(name, own_state[name].size(), param.size()))
15 def load(self, path): 16 self.load_state_dict(t.load(path))
107 def load(self, modelpath): 108 state = torch.load(modelpath) 109 self.load_state_dict(state['model']) 110 return state['optimizer']
74 def state_dict(self, **kwargs): 75 target = self.get_model() 76 return target.state_dict(**kwargs)
82 def load_state_dict(self, state_dict): 83 return self.optimizer.load_state_dict(state_dict)