import os import urllib import torch from torch.utils import model_zoo class CheckpointIO(object): ''' CheckpointIO class. It handles saving and loading checkpoints. Args: checkpoint_dir (str): path where checkpoints are saved ''' def __init__(self, checkpoint_dir='./chkpts', initialize_from=None, initialization_file_name='model_best.pt', **kwargs): self.module_dict = kwargs self.checkpoint_dir = checkpoint_dir self.initialize_from = initialize_from self.initialization_file_name = initialization_file_name if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) def register_modules(self, **kwargs): ''' Registers modules in current module dictionary. ''' self.module_dict.update(kwargs) def save(self, filename, **kwargs): ''' Saves the current module dictionary. Args: filename (str): name of output file ''' if not os.path.isabs(filename): filename = os.path.join(self.checkpoint_dir, filename) outdict = kwargs for k, v in self.module_dict.items(): outdict[k] = v.state_dict() torch.save(outdict, filename) def load(self, filename): '''Loads a module dictionary from local file or url. Args: filename (str): name of saved module dictionary ''' if is_url(filename): return self.load_url(filename) else: return self.load_file(filename) def load_file(self, filename): '''Loads a module dictionary from file. Args: filename (str): name of saved module dictionary ''' if not os.path.isabs(filename): filename = os.path.join(self.checkpoint_dir, filename) if os.path.exists(filename): print(filename) print('=> Loading checkpoint from local file...') state_dict = torch.load(filename) scalars = self.parse_state_dict(state_dict) return scalars else: if self.initialize_from is not None: self.initialize_weights() raise FileExistsError def load_url(self, url): '''Load a module dictionary from url. Args: url (str): url to saved model ''' print(url) print('=> Loading checkpoint from url...') state_dict = model_zoo.load_url(url, progress=True) scalars = self.parse_state_dict(state_dict) return scalars def parse_state_dict(self, state_dict): '''Parse state_dict of model and return scalars. Args: state_dict (dict): State dict of model ''' for k, v in self.module_dict.items(): if k in state_dict: v.load_state_dict(state_dict[k]) else: print('Warning: Could not find %s in checkpoint!' % k) scalars = {k: v for k, v in state_dict.items() if k not in self.module_dict} return scalars def initialize_weights(self): ''' Initializes the model weights from another model file. ''' print('Intializing weights from model %s' % self.initialize_from) filename_in = os.path.join( self.initialize_from, self.initialization_file_name) model_state_dict = self.module_dict.get('model').state_dict() model_dict = self.module_dict.get('model').state_dict() model_keys = set([k for (k, v) in model_dict.items()]) init_model_dict = torch.load(filename_in)['model'] init_model_k = set([k for (k, v) in init_model_dict.items()]) for k in model_keys: if ((k in init_model_k) and (model_state_dict[k].shape == init_model_dict[k].shape)): model_state_dict[k] = init_model_dict[k] self.module_dict.get('model').load_state_dict(model_state_dict) def is_url(url): ''' Checks if input is url.''' scheme = urllib.parse.urlparse(url).scheme return scheme in ('http', 'https')