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