| 35 | |
| 36 | |
| 37 | def load_checkpoint(config, model, optimizer, lr_scheduler, logger): |
| 38 | logger.info(f"==============> Resuming form {config.MODEL.RESUME}....................") |
| 39 | if config.MODEL.RESUME.startswith('https'): |
| 40 | checkpoint = torch.hub.load_state_dict_from_url( |
| 41 | config.MODEL.RESUME, map_location='cpu', check_hash=True) |
| 42 | else: |
| 43 | checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu') |
| 44 | msg = model.load_state_dict(checkpoint['model'], strict=False) |
| 45 | logger.info(msg) |
| 46 | max_accuracy = 0.0 |
| 47 | if not config.EVAL_MODE and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint: |
| 48 | optimizer.load_state_dict(checkpoint['optimizer']) |
| 49 | lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) |
| 50 | config.defrost() |
| 51 | config.TRAIN.START_EPOCH = checkpoint['epoch'] + 1 |
| 52 | config.freeze() |
| 53 | if 'amp' in checkpoint and config.AMP_OPT_LEVEL != "O0" and checkpoint['config'].AMP_OPT_LEVEL != "O0": |
| 54 | amp.load_state_dict(checkpoint['amp']) |
| 55 | logger.info(f"=> loaded successfully '{config.MODEL.RESUME}' (epoch {checkpoint['epoch']})") |
| 56 | if 'max_accuracy' in checkpoint: |
| 57 | max_accuracy = checkpoint['max_accuracy'] |
| 58 | |
| 59 | del checkpoint |
| 60 | torch.cuda.empty_cache() |
| 61 | return max_accuracy |
| 62 | |
| 63 | |
| 64 | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_scheduler, logger): |