raise ValueError('Unknown method') model = model.cuda() # load model start_epoch = params.start_epoch stop_epoch = params.stop_epoch if params.resume != '': resume_file = get_resume_file( '%s/checkpoints/%s' % (params.save_dir, params.resume), params.resume_epoch) if resume_file is not None: tmp = torch.load(resume_file) start_epoch = tmp['epoch'] + 1 model.load_state_dict(tmp['state']) print(' resume the training with at {} epoch (model file {})'. format(start_epoch, params.resume)) elif 'baseline' not in params.method: if params.warmup == 'gg3b0': raise Exception( 'Must provide the pre-trained feature encoder file using --warmup option!' ) state = load_warmup_state( '%s/checkpoints/%s' % (params.save_dir, params.warmup), params.method) model.feature.load_state_dict(state, strict=False) # training print('\n--- start the training ---') model = train(base_loader, val_loader, model, start_epoch, stop_epoch, params)
model.cuda() # resume training start_epoch = params.start_epoch stop_epoch = params.stop_epoch if params.resume != '': resume_file = get_resume_file( '%s/checkpoints/%s' % (params.save_dir, params.resume), params.resume_epoch) if resume_file is not None: start_epoch = model.resume(resume_file) print(' resume the training with at {} epoch (model file {})'. format(start_epoch, params.resume)) else: raise ValueError('No resume file') # load pre-trained feature encoder else: if params.warmup == 'gg3b0': raise Exception( 'Must provide pre-trained feature-encoder file using --warmup option!' ) model.model.feature.load_state_dict(load_warmup_state( '%s/checkpoints/%s' % (params.save_dir, params.warmup), params.method), strict=False) # training print('\n--- start the training ---') train(base_datamgr, datasets, val_loader, model, start_epoch, stop_epoch, params)
if torch.cuda.is_available(): model = model.cuda() # load model start_epoch = params.start_epoch #0 stop_epoch = params.stop_epoch #400 if params.resume != '': resume_file = get_resume_file( '%s/checkpoints/%s' % (params.save_dir, params.resume), params.resume_epoch) if resume_file is not None: tmp = torch.load(resume_file) start_epoch = tmp['epoch'] + 1 model.load_state_dict(tmp['state']) print(' resume the training with at {} epoch (model file {})'. format(start_epoch, params.resume)) elif 'baseline' not in params.method: if params.warmup == 'gg3b0': raise Exception( 'Must provide the pre-trained feature encoder file using --warmup option!' ) state = load_warmup_state( '%s/checkpoints/%s' % (params.save_dir, params.warmup), params.method) # modify feature extractor paras model.feature.load_state_dict(state, strict=False) # training print('\n--- start the training ---') model = train(base_loader, val_loader, model, start_epoch, stop_epoch, params)