# -------------------------------------------------------- # Swin Transformer # Copyright (c) 2021 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ze Liu # -------------------------------------------------------- import os import torch import torch.distributed as dist try: from torch._six import inf except: from torch import inf def load_checkpoint(config, model, optimizer, lr_scheduler, loss_scaler, 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') # checkpoint['model'].pop('head.weight') # checkpoint['model'].pop('head.bias') for k,v in checkpoint['model'].items(): print('=====',k,'======',v.shape) if checkpoint['model']['head.weight'].shape[0]==1000: checkpoint['model']['head.weight']=torch.nn.Parameter(torch.nn.init.xavier_uniform(torch.empty(2,768))) checkpoint['model']['head.bias']=torch.nn.Parameter(torch.randn(2)) print('===modify head weight and bias===') 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 'scaler' in checkpoint: loss_scaler.load_state_dict(checkpoint['scaler']) 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 load_pretrained(config, model, logger): logger.info(f"==============> Loading weight {config.MODEL.PRETRAINED} for fine-tuning......") checkpoint = torch.load(config.MODEL.PRETRAINED, map_location='cpu') state_dict = checkpoint['model'] # delete relative_position_index since we always re-init it relative_position_index_keys = [k for k in state_dict.keys() if "relative_position_index" in k] for k in relative_position_index_keys: del state_dict[k] # delete relative_coords_table since we always re-init it relative_position_index_keys = [k for k in state_dict.keys() if "relative_coords_table" in k] for k in relative_position_index_keys: del state_dict[k] # delete attn_mask since we always re-init it attn_mask_keys = [k for k in state_dict.keys() if "attn_mask" in k] for k in attn_mask_keys: del state_dict[k] # bicubic interpolate relative_position_bias_table if not match relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k] for k in relative_position_bias_table_keys: relative_position_bias_table_pretrained = state_dict[k] relative_position_bias_table_current = model.state_dict()[k] L1, nH1 = relative_position_bias_table_pretrained.size() L2, nH2 = relative_position_bias_table_current.size() if nH1 != nH2: logger.warning(f"Error in loading {k}, passing......") else: if L1 != L2: # bicubic interpolate relative_position_bias_table if not match S1 = int(L1 ** 0.5) S2 = int(L2 ** 0.5) relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate( relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(S2, S2), mode='bicubic') state_dict[k] = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0) # bicubic interpolate absolute_pos_embed if not match absolute_pos_embed_keys = [k for k in state_dict.keys() if "absolute_pos_embed" in k] for k in absolute_pos_embed_keys: # dpe absolute_pos_embed_pretrained = state_dict[k] absolute_pos_embed_current = model.state_dict()[k] _, L1, C1 = absolute_pos_embed_pretrained.size() _, L2, C2 = absolute_pos_embed_current.size() if C1 != C1: logger.warning(f"Error in loading {k}, passing......") else: if L1 != L2: S1 = int(L1 ** 0.5) S2 = int(L2 ** 0.5) absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(-1, S1, S1, C1) absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(0, 3, 1, 2) absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate( absolute_pos_embed_pretrained, size=(S2, S2), mode='bicubic') absolute_pos_embed_pretrained_resized = absolute_pos_embed_pretrained_resized.permute(0, 2, 3, 1) absolute_pos_embed_pretrained_resized = absolute_pos_embed_pretrained_resized.flatten(1, 2) state_dict[k] = absolute_pos_embed_pretrained_resized # check classifier, if not match, then re-init classifier to zero head_bias_pretrained = state_dict['head.bias'] Nc1 = head_bias_pretrained.shape[0] Nc2 = model.head.bias.shape[0] if (Nc1 != Nc2): if Nc1 == 21841 and Nc2 == 1000: logger.info("loading ImageNet-22K weight to ImageNet-1K ......") map22kto1k_path = f'data/map22kto1k.txt' with open(map22kto1k_path) as f: map22kto1k = f.readlines() map22kto1k = [int(id22k.strip()) for id22k in map22kto1k] state_dict['head.weight'] = state_dict['head.weight'][map22kto1k, :] state_dict['head.bias'] = state_dict['head.bias'][map22kto1k] else: torch.nn.init.constant_(model.head.bias, 0.) torch.nn.init.constant_(model.head.weight, 0.) del state_dict['head.weight'] del state_dict['head.bias'] logger.warning(f"Error in loading classifier head, re-init classifier head to 0") msg = model.load_state_dict(state_dict, strict=False) logger.warning(msg) logger.info(f"=> loaded successfully '{config.MODEL.PRETRAINED}'") del checkpoint torch.cuda.empty_cache() def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_scheduler, loss_scaler, logger): save_state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler.state_dict(), 'max_accuracy': max_accuracy, 'scaler': loss_scaler.state_dict(), 'epoch': epoch, 'config': config} 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 !!!") 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 ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch.Tensor: if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = [p for p in parameters if p.grad is not None] norm_type = float(norm_type) if len(parameters) == 0: return torch.tensor(0.) device = parameters[0].grad.device if norm_type == inf: total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters) else: total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type) return total_norm class NativeScalerWithGradNormCount: state_dict_key = "amp_scaler" def __init__(self): self._scaler = torch.cuda.amp.GradScaler() def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True): self._scaler.scale(loss).backward(create_graph=create_graph) if update_grad: if clip_grad is not None: assert parameters is not None self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad) else: self._scaler.unscale_(optimizer) norm = ampscaler_get_grad_norm(parameters) self._scaler.step(optimizer) self._scaler.update() else: norm = None return norm def state_dict(self): return self._scaler.state_dict() def load_state_dict(self, state_dict): self._scaler.load_state_dict(state_dict)