utils.py

# --------------------------------------------------------
# Focal Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Modified by Jianwei Yang (jianwyan@microsoft.com)
# Based on Swin Transformer written by Zhe Liu
# --------------------------------------------------------

import os
import torch
import torch.distributed as dist
from timm.models.layers import trunc_normal_

try:
    # noinspection PyUnresolvedReferences
    from apex import amp
except ImportError:
    amp = None


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 "focal" in config.MODEL.RESUME and 'model' not in checkpoint:
        checkpoint = {'model': checkpoint}
    if 'head.weight' in checkpoint['model']:
        if model.state_dict()['head.weight'].shape != checkpoint['model']['head.weight'].shape:
            # TODO: select the corresponding weights for 1K
            # checkpoint['model']['head.weight'] = checkpoint['model']['head.weight'].new(model.state_dict()['head.weight'].shape)
            # trunc_normal_(checkpoint['model']['head.weight'], std=.02)
            # checkpoint['model']['head.bias'] = checkpoint['model']['head.bias'].new(model.state_dict()['head.bias'].shape)
            # trunc_normal_(checkpoint['model']['head.bias'], std=.02)        
            checkpoint['model']['head.weight'] = model.state_dict()['head.weight'][:1000]
            checkpoint['model']['head.bias'] = model.state_dict()['head.bias'][:1000]

    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 !!!")


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 reduce_tensor_avg_meter(tensor, n=None):
    if n is None:
        n = dist.get_world_size()
    rt = tensor.clone()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    rt = rt / n
    return rt


class AverageMeter:
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
    
    def sync(self):
        rank = dist.get_rank()
        world_size = dist.get_world_size()
        val = torch.tensor(self.val).cuda()
        sum_v = torch.tensor(self.sum).cuda()
        count = torch.tensor(self.count).cuda()
        self.val = reduce_tensor_avg_meter(val, world_size).item()
        self.sum = reduce_tensor_avg_meter(sum_v, 1).item()
        self.count = reduce_tensor_avg_meter(count, 1).item()
        self.avg = self.sum / self.count