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| import numpy as np | |
| import io | |
| import os | |
| import json | |
| import logging | |
| import random | |
| import time | |
| from collections import defaultdict, deque | |
| import datetime | |
| from pathlib import Path | |
| from typing import List, Union | |
| import torch | |
| import torch.distributed as dist | |
| from .distributed import is_dist_avail_and_initialized | |
| logger = logging.getLogger(__name__) | |
| 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=20, fmt=None): | |
| if fmt is None: | |
| fmt = "{median:.4f} ({global_avg:.4f})" | |
| self.deque = deque(maxlen=window) | |
| 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] | |
| def median(self): | |
| d = torch.tensor(list(self.deque)) | |
| return d.median().item() | |
| def avg(self): | |
| d = torch.tensor(list(self.deque), dtype=torch.float32) | |
| return d.mean().item() | |
| def global_avg(self): | |
| return self.total / self.count | |
| def max(self): | |
| return max(self.deque) | |
| 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 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(): | |
| if meter.count == 0: # skip empty meter | |
| loss_str.append( | |
| "{}: {}".format(name, "No data") | |
| ) | |
| else: | |
| loss_str.append( | |
| "{}: {}".format(name, str(meter)) | |
| ) | |
| return self.delimiter.join(loss_str) | |
| def global_avg(self): | |
| loss_str = [] | |
| for name, meter in self.meters.items(): | |
| if meter.count == 0: | |
| loss_str.append( | |
| "{}: {}".format(name, "No data") | |
| ) | |
| else: | |
| loss_str.append( | |
| "{}: {:.4f}".format(name, meter.global_avg) | |
| ) | |
| return self.delimiter.join(loss_str) | |
| def get_global_avg_dict(self, prefix=""): | |
| """include a separator (e.g., `/`, or "_") at the end of `prefix`""" | |
| d = {f"{prefix}{k}": m.global_avg if m.count > 0 else 0. for k, m in self.meters.items()} | |
| return d | |
| 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, log_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} res mem: {res_mem:.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 % log_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(): | |
| logger.info(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, | |
| res_mem=torch.cuda.max_memory_reserved() / MB, | |
| )) | |
| else: | |
| logger.info(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))) | |
| logger.info('{} Total time: {} ({:.4f} s / it)'.format( | |
| header, total_time_str, total_time / len(iterable))) | |
| class AttrDict(dict): | |
| def __init__(self, *args, **kwargs): | |
| super(AttrDict, self).__init__(*args, **kwargs) | |
| self.__dict__ = self | |
| def compute_acc(logits, label, reduction='mean'): | |
| ret = (torch.argmax(logits, dim=1) == label).float() | |
| if reduction == 'none': | |
| return ret.detach() | |
| elif reduction == 'mean': | |
| return ret.mean().item() | |
| def compute_n_params(model, return_str=True): | |
| tot = 0 | |
| for p in model.parameters(): | |
| w = 1 | |
| for x in p.shape: | |
| w *= x | |
| tot += w | |
| if return_str: | |
| if tot >= 1e6: | |
| return '{:.1f}M'.format(tot / 1e6) | |
| else: | |
| return '{:.1f}K'.format(tot / 1e3) | |
| else: | |
| return tot | |
| def setup_seed(seed): | |
| torch.manual_seed(seed) | |
| np.random.seed(seed) | |
| random.seed(seed) | |
| def remove_files_if_exist(file_paths): | |
| for fp in file_paths: | |
| if os.path.isfile(fp): | |
| os.remove(fp) | |
| def save_json(data, filename, save_pretty=False, sort_keys=False): | |
| with open(filename, "w") as f: | |
| if save_pretty: | |
| f.write(json.dumps(data, indent=4, sort_keys=sort_keys)) | |
| else: | |
| json.dump(data, f) | |
| def load_json(filename): | |
| with open(filename, "r") as f: | |
| return json.load(f) | |
| def flat_list_of_lists(l): | |
| """flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]""" | |
| return [item for sublist in l for item in sublist] | |
| def find_files_by_suffix_recursively(root: str, suffix: Union[str, List[str]]): | |
| """ | |
| Args: | |
| root: path to the directory to start search files | |
| suffix: any str as suffix, or can match multiple such strings | |
| when input is List[str]. | |
| Example 1, e.g., suffix: `.jpg` or [`.jpg`, `.png`] | |
| Example 2, e.g., use a `*` in the `suffix`: `START*.jpg.`. | |
| """ | |
| if isinstance(suffix, str): | |
| suffix = [suffix, ] | |
| filepaths = flat_list_of_lists( | |
| [list(Path(root).rglob(f"*{e}")) for e in suffix]) | |
| return filepaths | |
| def match_key_and_shape(state_dict1, state_dict2): | |
| keys1 = set(state_dict1.keys()) | |
| keys2 = set(state_dict2.keys()) | |
| print(f"keys1 - keys2: {keys1 - keys2}") | |
| print(f"keys2 - keys1: {keys2 - keys1}") | |
| mismatch = 0 | |
| for k in list(keys1): | |
| if state_dict1[k].shape != state_dict2[k].shape: | |
| print( | |
| f"k={k}, state_dict1[k].shape={state_dict1[k].shape}, state_dict2[k].shape={state_dict2[k].shape}") | |
| mismatch += 1 | |
| print(f"mismatch {mismatch}") | |
| def merge_dicts(list_dicts): | |
| merged_dict = list_dicts[0].copy() | |
| for i in range(1, len(list_dicts)): | |
| merged_dict.update(list_dicts[i]) | |
| return merged_dict | |