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307 lines
12 KiB
307 lines
12 KiB
from copy import deepcopy
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import torch
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import math
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import os
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import re
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import shutil
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import glob
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import csv
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import operator
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import logging
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import numpy as np
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from collections import OrderedDict
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try:
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from apex import amp
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has_apex = True
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except ImportError:
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amp = None
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has_apex = False
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from torch import distributed as dist
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def unwrap_model(model):
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if isinstance(model, ModelEma):
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return unwrap_model(model.ema)
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else:
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return model.module if hasattr(model, 'module') else model
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def get_state_dict(model):
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return unwrap_model(model).state_dict()
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class CheckpointSaver:
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def __init__(
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self,
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checkpoint_prefix='checkpoint',
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recovery_prefix='recovery',
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checkpoint_dir='',
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recovery_dir='',
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decreasing=False,
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max_history=10):
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# state
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self.checkpoint_files = [] # (filename, metric) tuples in order of decreasing betterness
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self.best_epoch = None
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self.best_metric = None
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self.curr_recovery_file = ''
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self.last_recovery_file = ''
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# config
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self.checkpoint_dir = checkpoint_dir
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self.recovery_dir = recovery_dir
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self.save_prefix = checkpoint_prefix
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self.recovery_prefix = recovery_prefix
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self.extension = '.pth.tar'
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self.decreasing = decreasing # a lower metric is better if True
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self.cmp = operator.lt if decreasing else operator.gt # True if lhs better than rhs
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self.max_history = max_history
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assert self.max_history >= 1
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def save_checkpoint(self, model, optimizer, args, epoch, model_ema=None, metric=None, use_amp=False):
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assert epoch >= 0
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worst_file = self.checkpoint_files[-1] if self.checkpoint_files else None
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if (len(self.checkpoint_files) < self.max_history
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or metric is None or self.cmp(metric, worst_file[1])):
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if len(self.checkpoint_files) >= self.max_history:
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self._cleanup_checkpoints(1)
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filename = '-'.join([self.save_prefix, str(epoch)]) + self.extension
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save_path = os.path.join(self.checkpoint_dir, filename)
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self._save(save_path, model, optimizer, args, epoch, model_ema, metric, use_amp)
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self.checkpoint_files.append((save_path, metric))
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self.checkpoint_files = sorted(
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self.checkpoint_files, key=lambda x: x[1],
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reverse=not self.decreasing) # sort in descending order if a lower metric is not better
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checkpoints_str = "Current checkpoints:\n"
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for c in self.checkpoint_files:
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checkpoints_str += ' {}\n'.format(c)
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logging.info(checkpoints_str)
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if metric is not None and (self.best_metric is None or self.cmp(metric, self.best_metric)):
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self.best_epoch = epoch
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self.best_metric = metric
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shutil.copyfile(save_path, os.path.join(self.checkpoint_dir, 'model_best' + self.extension))
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return (None, None) if self.best_metric is None else (self.best_metric, self.best_epoch)
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def _save(self, save_path, model, optimizer, args, epoch, model_ema=None, metric=None, use_amp=False):
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save_state = {
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'epoch': epoch,
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'arch': args.model,
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'state_dict': get_state_dict(model),
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'optimizer': optimizer.state_dict(),
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'args': args,
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'version': 2, # version < 2 increments epoch before save
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}
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if use_amp and 'state_dict' in amp.__dict__:
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save_state['amp'] = amp.state_dict()
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if model_ema is not None:
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save_state['state_dict_ema'] = get_state_dict(model_ema)
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if metric is not None:
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save_state['metric'] = metric
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torch.save(save_state, save_path)
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def _cleanup_checkpoints(self, trim=0):
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trim = min(len(self.checkpoint_files), trim)
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delete_index = self.max_history - trim
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if delete_index <= 0 or len(self.checkpoint_files) <= delete_index:
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return
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to_delete = self.checkpoint_files[delete_index:]
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for d in to_delete:
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try:
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logging.debug("Cleaning checkpoint: {}".format(d))
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os.remove(d[0])
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except Exception as e:
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logging.error("Exception '{}' while deleting checkpoint".format(e))
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self.checkpoint_files = self.checkpoint_files[:delete_index]
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def save_recovery(self, model, optimizer, args, epoch, model_ema=None, use_amp=False, batch_idx=0):
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assert epoch >= 0
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filename = '-'.join([self.recovery_prefix, str(epoch), str(batch_idx)]) + self.extension
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save_path = os.path.join(self.recovery_dir, filename)
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self._save(save_path, model, optimizer, args, epoch, model_ema, use_amp=use_amp)
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if os.path.exists(self.last_recovery_file):
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try:
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logging.debug("Cleaning recovery: {}".format(self.last_recovery_file))
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os.remove(self.last_recovery_file)
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except Exception as e:
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logging.error("Exception '{}' while removing {}".format(e, self.last_recovery_file))
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self.last_recovery_file = self.curr_recovery_file
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self.curr_recovery_file = save_path
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def find_recovery(self):
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recovery_path = os.path.join(self.recovery_dir, self.recovery_prefix)
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files = glob.glob(recovery_path + '*' + self.extension)
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files = sorted(files)
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if len(files):
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return files[0]
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else:
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return ''
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class AverageMeter:
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def accuracy(output, target, topk=(1,)):
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"""Computes the precision@k for the specified values of k"""
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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return [correct[:k].view(-1).float().sum(0) * 100. / batch_size for k in topk]
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def get_outdir(path, *paths, inc=False):
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outdir = os.path.join(path, *paths)
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if not os.path.exists(outdir):
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os.makedirs(outdir)
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elif inc:
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count = 1
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outdir_inc = outdir + '-' + str(count)
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while os.path.exists(outdir_inc):
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count = count + 1
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outdir_inc = outdir + '-' + str(count)
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assert count < 100
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outdir = outdir_inc
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os.makedirs(outdir)
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return outdir
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def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=False):
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rowd = OrderedDict(epoch=epoch)
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rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
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rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
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with open(filename, mode='a') as cf:
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dw = csv.DictWriter(cf, fieldnames=rowd.keys())
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if write_header: # first iteration (epoch == 1 can't be used)
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dw.writeheader()
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dw.writerow(rowd)
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def natural_key(string_):
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"""See http://www.codinghorror.com/blog/archives/001018.html"""
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return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
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def reduce_tensor(tensor, n):
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rt = tensor.clone()
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dist.all_reduce(rt, op=dist.ReduceOp.SUM)
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rt /= n
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return rt
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def distribute_bn(model, world_size, reduce=False):
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# ensure every node has the same running bn stats
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for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
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if ('running_mean' in bn_name) or ('running_var' in bn_name):
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if reduce:
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# average bn stats across whole group
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torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
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bn_buf /= float(world_size)
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else:
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# broadcast bn stats from rank 0 to whole group
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torch.distributed.broadcast(bn_buf, 0)
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class ModelEma:
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""" Model Exponential Moving Average
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Keep a moving average of everything in the model state_dict (parameters and buffers).
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This is intended to allow functionality like
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https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
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A smoothed version of the weights is necessary for some training schemes to perform well.
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E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
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RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
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smoothing of weights to match results. Pay attention to the decay constant you are using
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relative to your update count per epoch.
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To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
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disable validation of the EMA weights. Validation will have to be done manually in a separate
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process, or after the training stops converging.
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This class is sensitive where it is initialized in the sequence of model init,
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GPU assignment and distributed training wrappers.
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I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.
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"""
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def __init__(self, model, decay=0.9999, device='', resume=''):
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# make a copy of the model for accumulating moving average of weights
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self.ema = deepcopy(model)
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self.ema.eval()
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self.decay = decay
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self.device = device # perform ema on different device from model if set
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if device:
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self.ema.to(device=device)
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self.ema_has_module = hasattr(self.ema, 'module')
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if resume:
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self._load_checkpoint(resume)
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for p in self.ema.parameters():
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p.requires_grad_(False)
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def _load_checkpoint(self, checkpoint_path):
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checkpoint = torch.load(checkpoint_path)
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assert isinstance(checkpoint, dict)
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if 'state_dict_ema' in checkpoint:
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new_state_dict = OrderedDict()
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for k, v in checkpoint['state_dict_ema'].items():
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# ema model may have been wrapped by DataParallel, and need module prefix
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if self.ema_has_module:
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name = 'module.' + k if not k.startswith('module') else k
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else:
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name = k
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new_state_dict[name] = v
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self.ema.load_state_dict(new_state_dict)
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logging.info("Loaded state_dict_ema")
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else:
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logging.warning("Failed to find state_dict_ema, starting from loaded model weights")
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def update(self, model):
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# correct a mismatch in state dict keys
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needs_module = hasattr(model, 'module') and not self.ema_has_module
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with torch.no_grad():
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msd = model.state_dict()
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for k, ema_v in self.ema.state_dict().items():
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if needs_module:
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k = 'module.' + k
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model_v = msd[k].detach()
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if self.device:
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model_v = model_v.to(device=self.device)
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ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)
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class FormatterNoInfo(logging.Formatter):
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def __init__(self, fmt='%(levelname)s: %(message)s'):
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logging.Formatter.__init__(self, fmt)
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def format(self, record):
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if record.levelno == logging.INFO:
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return str(record.getMessage())
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return logging.Formatter.format(self, record)
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def setup_default_logging(default_level=logging.INFO):
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console_handler = logging.StreamHandler()
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console_handler.setFormatter(FormatterNoInfo())
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logging.root.addHandler(console_handler)
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logging.root.setLevel(default_level)
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