utils.py

# --------------------------------------------------------
# RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (https://arxiv.org/abs/2112.11081)
# CVPR 2022
# Github source: https://github.com/DingXiaoH/RepMLP
# Licensed under The MIT License [see LICENSE for details]
# The training script is based on the code of Swin Transformer (https://github.com/microsoft/Swin-Transformer)
# --------------------------------------------------------

import os
import torch
import torch.distributed as dist
import numpy as np

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


def unwrap_model(model):
    """Remove the DistributedDataParallel wrapper if present."""
    wrapped = isinstance(model, torch.nn.parallel.distributed.DistributedDataParallel)
    return model.module if wrapped else model


def load_checkpoint(config, model, optimizer, lr_scheduler, logger, model_ema=None):
    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')
    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']
    if model_ema is not None:
        unwrap_model(model_ema).load_state_dict(checkpoint['ema'])
        print('=================================================== EMAloaded')

    del checkpoint
    torch.cuda.empty_cache()
    return max_accuracy

def load_weights(model, path):
    checkpoint = torch.load(path, map_location='cpu')
    if 'model' in checkpoint:
        checkpoint = checkpoint['model']
    unwrap_model(model).load_state_dict(checkpoint, strict=False)
    print('=================== loaded from', path)


def save_latest(config, epoch, model, max_accuracy, optimizer, lr_scheduler, logger, model_ema=None):
    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()
    if model_ema is not None:
        save_state['ema'] = unwrap_model(model_ema).state_dict()

    save_path = os.path.join(config.OUTPUT, 'latest.pth')
    logger.info(f"{save_path} saving......")
    torch.save(save_state, save_path)
    logger.info(f"{save_path} saved !!!")

def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_scheduler, logger, is_best=False, model_ema=None):
    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()
    if model_ema is not None:
        save_state['ema'] = unwrap_model(model_ema).state_dict()

    if is_best:
        best_path = os.path.join(config.OUTPUT, 'best_ckpt.pth')
        torch.save(save_state, best_path)

    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') and 'ema' not in ckpt]
    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 update_model_ema(cfg, num_gpus, model, model_ema, cur_epoch, cur_iter):
    """Update exponential moving average (ema) of model weights."""
    update_period = cfg.TRAIN.EMA_UPDATE_PERIOD
    if update_period is None or update_period == 0 or cur_iter % update_period != 0:
        return
    # Adjust alpha to be fairly independent of other parameters
    total_batch_size = num_gpus * cfg.DATA.BATCH_SIZE
    adjust = total_batch_size / cfg.TRAIN.EPOCHS * update_period
    # print('ema adjust', adjust)
    alpha = min(1.0, cfg.TRAIN.EMA_ALPHA * adjust)
    # During warmup simply copy over weights instead of using ema
    alpha = 1.0 if cur_epoch < cfg.TRAIN.WARMUP_EPOCHS else alpha
    # Take ema of all parameters (not just named parameters)
    params = unwrap_model(model).state_dict()
    for name, param in unwrap_model(model_ema).state_dict().items():
        param.copy_(param * (1.0 - alpha) + params[name] * alpha)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        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 __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)