| import builtins |
| import datetime |
| import os |
| import time |
| from collections import defaultdict, deque |
| from pathlib import Path |
| import copy |
|
|
| import torch |
| import torch.distributed as dist |
|
|
|
|
| class SmoothedValue(object): |
| """Track a series of values and provide access to smoothed values over a |
| window or the global series average. |
| """ |
|
|
| def __init__(self, window_size=20, fmt=None): |
| if fmt is None: |
| fmt = "{median:.4f} ({global_avg:.4f})" |
| self.deque = deque(maxlen=window_size) |
| self.total = 0.0 |
| self.count = 0 |
| self.fmt = fmt |
|
|
| def update(self, value, n=1): |
| self.deque.append(value) |
| self.count += n |
| self.total += value * n |
|
|
| def synchronize_between_processes(self): |
| """ |
| Warning: does not synchronize the deque! |
| """ |
| if not is_dist_avail_and_initialized(): |
| return |
| t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') |
| dist.barrier() |
| dist.all_reduce(t) |
| t = t.tolist() |
| self.count = int(t[0]) |
| self.total = t[1] |
|
|
| @property |
| def median(self): |
| d = torch.tensor(list(self.deque)) |
| return d.median().item() |
|
|
| @property |
| def avg(self): |
| d = torch.tensor(list(self.deque), dtype=torch.float32) |
| return d.mean().item() |
|
|
| @property |
| def global_avg(self): |
| return self.total / self.count |
|
|
| @property |
| def max(self): |
| return max(self.deque) |
|
|
| @property |
| def value(self): |
| return self.deque[-1] |
|
|
| def __str__(self): |
| return self.fmt.format( |
| median=self.median, |
| avg=self.avg, |
| global_avg=self.global_avg, |
| max=self.max, |
| value=self.value) |
|
|
|
|
| class MetricLogger(object): |
| def __init__(self, delimiter="\t"): |
| self.meters = defaultdict(SmoothedValue) |
| self.delimiter = delimiter |
|
|
| def update(self, **kwargs): |
| for k, v in kwargs.items(): |
| if v is None: |
| continue |
| if isinstance(v, torch.Tensor): |
| v = v.item() |
| assert isinstance(v, (float, int)) |
| self.meters[k].update(v) |
|
|
| def __getattr__(self, attr): |
| if attr in self.meters: |
| return self.meters[attr] |
| if attr in self.__dict__: |
| return self.__dict__[attr] |
| raise AttributeError("'{}' object has no attribute '{}'".format( |
| type(self).__name__, attr)) |
|
|
| def __str__(self): |
| loss_str = [] |
| for name, meter in self.meters.items(): |
| loss_str.append( |
| "{}: {}".format(name, str(meter)) |
| ) |
| return self.delimiter.join(loss_str) |
|
|
| def synchronize_between_processes(self): |
| for meter in self.meters.values(): |
| meter.synchronize_between_processes() |
|
|
| def add_meter(self, name, meter): |
| self.meters[name] = meter |
|
|
| def log_every(self, iterable, print_freq, header=None): |
| i = 0 |
| if not header: |
| header = '' |
| start_time = time.time() |
| end = time.time() |
| iter_time = SmoothedValue(fmt='{avg:.4f}') |
| data_time = SmoothedValue(fmt='{avg:.4f}') |
| space_fmt = ':' + str(len(str(len(iterable)))) + 'd' |
| log_msg = [ |
| header, |
| '[{0' + space_fmt + '}/{1}]', |
| 'eta: {eta}', |
| '{meters}', |
| 'time: {time}', |
| 'data: {data}' |
| ] |
| if torch.cuda.is_available(): |
| log_msg.append('max mem: {memory:.0f}') |
| log_msg = self.delimiter.join(log_msg) |
| MB = 1024.0 * 1024.0 |
| for obj in iterable: |
| data_time.update(time.time() - end) |
| yield obj |
| iter_time.update(time.time() - end) |
| if i % print_freq == 0 or i == len(iterable) - 1: |
| eta_seconds = iter_time.global_avg * (len(iterable) - i) |
| eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
| if torch.cuda.is_available(): |
| print(log_msg.format( |
| i, len(iterable), eta=eta_string, |
| meters=str(self), |
| time=str(iter_time), data=str(data_time), |
| memory=torch.cuda.max_memory_allocated() / MB)) |
| else: |
| print(log_msg.format( |
| i, len(iterable), eta=eta_string, |
| meters=str(self), |
| time=str(iter_time), data=str(data_time))) |
| i += 1 |
| end = time.time() |
| total_time = time.time() - start_time |
| total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
| print('{} Total time: {} ({:.4f} s / it)'.format( |
| header, total_time_str, total_time / len(iterable))) |
|
|
|
|
| def setup_for_distributed(is_master): |
| """ |
| This function disables printing when not in master process |
| """ |
| builtin_print = builtins.print |
|
|
| def print(*args, **kwargs): |
| force = kwargs.pop('force', False) |
| force = force or (get_world_size() > 8) |
| if is_master or force: |
| now = datetime.datetime.now().time() |
| builtin_print('[{}] '.format(now), end='') |
| builtin_print(*args, **kwargs) |
|
|
| builtins.print = print |
|
|
|
|
| def is_dist_avail_and_initialized(): |
| if not dist.is_available(): |
| return False |
| if not dist.is_initialized(): |
| return False |
| return True |
|
|
|
|
| def get_world_size(): |
| if not is_dist_avail_and_initialized(): |
| return 1 |
| return dist.get_world_size() |
|
|
|
|
| def get_rank(): |
| if not is_dist_avail_and_initialized(): |
| return 0 |
| return dist.get_rank() |
|
|
|
|
| def is_main_process(): |
| return get_rank() == 0 |
|
|
|
|
| def save_on_master(*args, **kwargs): |
| if is_main_process(): |
| torch.save(*args, **kwargs) |
|
|
|
|
| def init_distributed_mode(args): |
| if args.dist_on_itp: |
| args.rank = int(os.environ['OMPI_COMM_WORLD_RANK']) |
| args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE']) |
| args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK']) |
| args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT']) |
| os.environ['LOCAL_RANK'] = str(args.gpu) |
| os.environ['RANK'] = str(args.rank) |
| os.environ['WORLD_SIZE'] = str(args.world_size) |
| |
| elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: |
| args.rank = int(os.environ["RANK"]) |
| args.world_size = int(os.environ['WORLD_SIZE']) |
| args.gpu = int(os.environ['LOCAL_RANK']) |
| elif 'SLURM_PROCID' in os.environ: |
| args.rank = int(os.environ['SLURM_PROCID']) |
| args.gpu = args.rank % torch.cuda.device_count() |
| else: |
| print('Not using distributed mode') |
| setup_for_distributed(is_master=True) |
| args.distributed = False |
| return |
|
|
| args.distributed = True |
|
|
| torch.cuda.set_device(args.gpu) |
| args.dist_backend = 'nccl' |
| print('| distributed init (rank {}): {}, gpu {}'.format( |
| args.rank, args.dist_url, args.gpu), flush=True) |
| torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, |
| world_size=args.world_size, rank=args.rank) |
| torch.distributed.barrier() |
| setup_for_distributed(args.rank == 0) |
|
|
|
|
| def add_weight_decay(model, weight_decay=0, skip_list=()): |
| decay = [] |
| no_decay = [] |
| for name, param in model.named_parameters(): |
| if not param.requires_grad: |
| continue |
| if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list or 'diffloss' in name: |
| no_decay.append(param) |
| else: |
| decay.append(param) |
| return [ |
| {'params': no_decay, 'weight_decay': 0.}, |
| {'params': decay, 'weight_decay': weight_decay}] |
|
|
|
|
| def save_model(args, model_without_ddp, optimizer, epoch, epoch_name=None): |
| if epoch_name is None: |
| epoch_name = str(epoch) |
| output_dir = Path(args.output_dir) |
| checkpoint_path = output_dir / ('checkpoint-%s.pth' % epoch_name) |
|
|
| to_save = { |
| 'model': model_without_ddp.state_dict(), |
| 'optimizer': optimizer.state_dict(), |
| 'epoch': epoch, |
| 'args': args, |
| } |
|
|
| |
| ema_state_dict1 = copy.deepcopy(model_without_ddp.state_dict()) |
| ema_state_dict2 = copy.deepcopy(model_without_ddp.state_dict()) |
| for i, (name, _value) in enumerate(model_without_ddp.named_parameters()): |
| assert name in ema_state_dict1 and name in ema_state_dict2 |
| ema_state_dict1[name] = model_without_ddp.ema_params1[i] |
| ema_state_dict2[name] = model_without_ddp.ema_params2[i] |
| to_save['model_ema1'] = ema_state_dict1 |
| to_save['model_ema2'] = ema_state_dict2 |
|
|
| save_on_master(to_save, checkpoint_path) |
|
|
|
|
| def all_reduce_mean(x): |
| world_size = get_world_size() |
| if world_size > 1: |
| x_reduce = torch.tensor(x).cuda() |
| dist.all_reduce(x_reduce) |
| x_reduce /= world_size |
| return x_reduce.item() |
| else: |
| return x |
