import os import torch import importlib import torch.distributed as dist try: # noinspection PyUnresolvedReferences from apex import amp except ImportError: amp = None def relative_bias_interpolate(checkpoint,config): for k in list(checkpoint['model']): if 'relative_position_index' in k: del checkpoint['model'][k] if 'relative_position_bias_table' in k: relative_position_bias_table = checkpoint['model'][k] cls_bias = relative_position_bias_table[:1,:] relative_position_bias_table = relative_position_bias_table[1:,:] size = int(relative_position_bias_table.shape[0]**0.5) img_size = (size+1)//2 if 'stage_3' in k: downsample_ratio = 16 elif 'stage_4' in k: downsample_ratio = 32 new_img_size = config.DATA.IMG_SIZE//downsample_ratio new_size = 2*new_img_size-1 if new_size == size: continue relative_position_bias_table = relative_position_bias_table.reshape(size,size,-1) relative_position_bias_table = relative_position_bias_table.unsqueeze(0).permute(0,3,1,2)#bs,nhead,h,w relative_position_bias_table = torch.nn.functional.interpolate( relative_position_bias_table, size=(new_size, new_size), mode='bicubic', align_corners=False) relative_position_bias_table = relative_position_bias_table.permute(0,2,3,1) relative_position_bias_table = relative_position_bias_table.squeeze(0).reshape(new_size*new_size,-1) relative_position_bias_table = torch.cat((cls_bias,relative_position_bias_table),dim=0) checkpoint['model'][k] = relative_position_bias_table return checkpoint def load_pretained(config,model,logger=None,strict=False): if logger is not None: logger.info(f"==============> pretrain form {config.MODEL.PRETRAINED}....................") checkpoint = torch.load(config.MODEL.PRETRAINED, map_location='cpu') if 'model' not in checkpoint: if 'state_dict_ema' in checkpoint: checkpoint['model'] = checkpoint['state_dict_ema'] else: checkpoint['model'] = checkpoint if config.MODEL.DORP_HEAD: if 'head.weight' in checkpoint['model'] and 'head.bias' in checkpoint['model']: if logger is not None: logger.info(f"==============> drop head....................") del checkpoint['model']['head.weight'] del checkpoint['model']['head.bias'] if 'head.fc.weight' in checkpoint['model'] and 'head.fc.bias' in checkpoint['model']: if logger is not None: logger.info(f"==============> drop head....................") del checkpoint['model']['head.fc.weight'] del checkpoint['model']['head.fc.bias'] if config.MODEL.DORP_META: if logger is not None: logger.info(f"==============> drop meta head....................") for k in list(checkpoint['model']): if 'meta' in k: del checkpoint['model'][k] checkpoint = relative_bias_interpolate(checkpoint,config) if 'point_coord' in checkpoint['model']: if logger is not None: logger.info(f"==============> drop point coord....................") del checkpoint['model']['point_coord'] msg = model.load_state_dict(checkpoint['model'], strict=strict) del checkpoint torch.cuda.empty_cache() def load_checkpoint(config, model, optimizer, lr_scheduler, logger): logger.info(f"==============> Resuming form {config.MODEL.RESUME}....................") if config.MODEL.RESUME.startswith('https'): checkpoint = torch.hub.load_state_dict_from_url( config.MODEL.RESUME, map_location='cpu', check_hash=True) else: checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu') if 'model' not in checkpoint: if 'state_dict_ema' in checkpoint: checkpoint['model'] = checkpoint['state_dict_ema'] else: checkpoint['model'] = checkpoint msg = model.load_state_dict(checkpoint['model'], strict=False) logger.info(msg) max_accuracy = 0.0 if not config.EVAL_MODE and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint: optimizer.load_state_dict(checkpoint['optimizer']) lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) config.defrost() config.TRAIN.START_EPOCH = checkpoint['epoch'] + 1 config.freeze() if 'amp' in checkpoint and config.AMP_OPT_LEVEL != "O0" and checkpoint['config'].AMP_OPT_LEVEL != "O0": amp.load_state_dict(checkpoint['amp']) logger.info(f"=> loaded successfully '{config.MODEL.RESUME}' (epoch {checkpoint['epoch']})") if 'max_accuracy' in checkpoint: max_accuracy = checkpoint['max_accuracy'] del checkpoint torch.cuda.empty_cache() return max_accuracy def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_scheduler, logger): save_state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler.state_dict(), 'max_accuracy': max_accuracy, 'epoch': epoch, 'config': config} if config.AMP_OPT_LEVEL != "O0": save_state['amp'] = amp.state_dict() save_path = os.path.join(config.OUTPUT, f'ckpt_epoch_{epoch}.pth') logger.info(f"{save_path} saving......") torch.save(save_state, save_path) logger.info(f"{save_path} saved !!!") lastest_save_path = os.path.join(config.OUTPUT, f'latest.pth') logger.info(f"{lastest_save_path} saving......") torch.save(save_state, lastest_save_path) logger.info(f"{lastest_save_path} saved !!!") def get_grad_norm(parameters, norm_type=2): if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = list(filter(lambda p: p.grad is not None, parameters)) norm_type = float(norm_type) total_norm = 0 for p in parameters: param_norm = p.grad.data.norm(norm_type) total_norm += param_norm.item() ** norm_type total_norm = total_norm ** (1. / norm_type) return total_norm def auto_resume_helper(output_dir): checkpoints = os.listdir(output_dir) checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')] print(f"All checkpoints founded in {output_dir}: {checkpoints}") if len(checkpoints) > 0: latest_checkpoint = max([os.path.join(output_dir, d) for d in checkpoints], key=os.path.getmtime) print(f"The latest checkpoint founded: {latest_checkpoint}") resume_file = latest_checkpoint else: resume_file = None return resume_file def reduce_tensor(tensor): rt = tensor.clone() dist.all_reduce(rt, op=dist.ReduceOp.SUM) rt /= dist.get_world_size() return rt def load_ext(name, funcs): ext = importlib.import_module(name) for fun in funcs: assert hasattr(ext, fun), f'{fun} miss in module {name}' return ext