def prep_net(self, gpu_id=None, path='',dist=False): import torch import models.pytorch.model as model print('path = %s' % (path)) print('Model set! dist mode? ', dist) self.net = model.SIGGRAPHGenerator(dist=dist) self.net.load_state_dict(torch.load(path)) if gpu_id!=-1: self.net.cuda() self.net.eval() self.net_set = True
def prep_net(self, gpu_id=None, path='', dist=False): import torch import models.pytorch.model as model print('path = %s' % path) print('Model set! dist mode? ', dist) self.net = model.SIGGRAPHGenerator(dist=dist) state_dict = torch.load(path) if hasattr(state_dict, '_metadata'): del state_dict._metadata # patch InstanceNorm checkpoints prior to 0.4 for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop self.__patch_instance_norm_state_dict(state_dict, self.net, key.split('.')) self.net.load_state_dict(state_dict) if gpu_id != -1: self.net.cuda() self.net.eval() self.net_set = True