| import datetime |
| import functools |
| import glob |
| import os |
| import subprocess |
| import sys |
| import time |
| from collections import defaultdict, deque |
| from typing import Iterator, List, Tuple |
|
|
| import numpy as np |
| import pytz |
| import torch |
| import torch.distributed as tdist |
|
|
| import dist |
| from utils import arg_util |
|
|
| os_system = functools.partial(subprocess.call, shell=True) |
| def echo(info): |
| os_system(f'echo "[$(date "+%m-%d-%H:%M:%S")] ({os.path.basename(sys._getframe().f_back.f_code.co_filename)}, line{sys._getframe().f_back.f_lineno})=> {info}"') |
| def os_system_get_stdout(cmd): |
| return subprocess.run(cmd, shell=True, stdout=subprocess.PIPE).stdout.decode('utf-8') |
| def os_system_get_stdout_stderr(cmd): |
| cnt = 0 |
| while True: |
| try: |
| sp = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=30) |
| except subprocess.TimeoutExpired: |
| cnt += 1 |
| print(f'[fetch free_port file] timeout cnt={cnt}') |
| else: |
| return sp.stdout.decode('utf-8'), sp.stderr.decode('utf-8') |
|
|
|
|
| def time_str(fmt='[%m-%d %H:%M:%S]'): |
| return datetime.datetime.now(tz=pytz.timezone('Asia/Shanghai')).strftime(fmt) |
|
|
|
|
| def init_distributed_mode(local_out_path, only_sync_master=False, timeout=30): |
| try: |
| dist.initialize(fork=False, timeout=timeout) |
| dist.barrier() |
| except RuntimeError: |
| print(f'{">"*75} NCCL Error {"<"*75}', flush=True) |
| time.sleep(10) |
| |
| if local_out_path is not None: os.makedirs(local_out_path, exist_ok=True) |
| _change_builtin_print(dist.is_local_master()) |
| if (dist.is_master() if only_sync_master else dist.is_local_master()) and local_out_path is not None and len(local_out_path): |
| sys.stdout, sys.stderr = SyncPrint(local_out_path, sync_stdout=True), SyncPrint(local_out_path, sync_stdout=False) |
|
|
|
|
| def _change_builtin_print(is_master): |
| import builtins as __builtin__ |
| |
| builtin_print = __builtin__.print |
| if type(builtin_print) != type(open): |
| return |
| |
| def prt(*args, **kwargs): |
| force = kwargs.pop('force', False) |
| clean = kwargs.pop('clean', False) |
| deeper = kwargs.pop('deeper', False) |
| if is_master or force: |
| if not clean: |
| f_back = sys._getframe().f_back |
| if deeper and f_back.f_back is not None: |
| f_back = f_back.f_back |
| file_desc = f'{f_back.f_code.co_filename:24s}'[-24:] |
| builtin_print(f'{time_str()} ({file_desc}, line{f_back.f_lineno:-4d})=>', *args, **kwargs) |
| else: |
| builtin_print(*args, **kwargs) |
| |
| __builtin__.print = prt |
|
|
|
|
| class SyncPrint(object): |
| def __init__(self, local_output_dir, sync_stdout=True): |
| self.sync_stdout = sync_stdout |
| self.terminal_stream = sys.stdout if sync_stdout else sys.stderr |
| fname = os.path.join(local_output_dir, 'stdout.txt' if sync_stdout else 'stderr.txt') |
| existing = os.path.exists(fname) |
| self.file_stream = open(fname, 'a') |
| if existing: |
| self.file_stream.write('\n'*7 + '='*55 + f' RESTART {time_str()} ' + '='*55 + '\n') |
| self.file_stream.flush() |
| self.enabled = True |
| |
| def write(self, message): |
| self.terminal_stream.write(message) |
| self.file_stream.write(message) |
| |
| def flush(self): |
| self.terminal_stream.flush() |
| self.file_stream.flush() |
| |
| def close(self): |
| if not self.enabled: |
| return |
| self.enabled = False |
| self.file_stream.flush() |
| self.file_stream.close() |
| if self.sync_stdout: |
| sys.stdout = self.terminal_stream |
| sys.stdout.flush() |
| else: |
| sys.stderr = self.terminal_stream |
| sys.stderr.flush() |
| |
| def __del__(self): |
| self.close() |
|
|
|
|
| class DistLogger(object): |
| def __init__(self, lg, verbose): |
| self._lg, self._verbose = lg, verbose |
| |
| @staticmethod |
| def do_nothing(*args, **kwargs): |
| pass |
| |
| def __getattr__(self, attr: str): |
| return getattr(self._lg, attr) if self._verbose else DistLogger.do_nothing |
|
|
|
|
| class TensorboardLogger(object): |
| def __init__(self, log_dir, filename_suffix): |
| try: import tensorflow_io as tfio |
| except: pass |
| from torch.utils.tensorboard import SummaryWriter |
| self.writer = SummaryWriter(log_dir=log_dir, filename_suffix=filename_suffix) |
| self.step = 0 |
| |
| def set_step(self, step=None): |
| if step is not None: |
| self.step = step |
| else: |
| self.step += 1 |
| |
| def update(self, head='scalar', step=None, **kwargs): |
| for k, v in kwargs.items(): |
| if v is None: |
| continue |
| |
| if step is None: |
| it = self.step |
| if it == 0 or (it + 1) % 500 == 0: |
| if hasattr(v, 'item'): v = v.item() |
| self.writer.add_scalar(f'{head}/{k}', v, it) |
| else: |
| if hasattr(v, 'item'): v = v.item() |
| self.writer.add_scalar(f'{head}/{k}', v, step) |
| |
| def log_tensor_as_distri(self, tag, tensor1d, step=None): |
| if step is None: |
| step = self.step |
| loggable = step == 0 or (step + 1) % 500 == 0 |
| else: |
| loggable = True |
| if loggable: |
| try: |
| self.writer.add_histogram(tag=tag, values=tensor1d, global_step=step) |
| except Exception as e: |
| print(f'[log_tensor_as_distri writer.add_histogram failed]: {e}') |
| |
| def log_image(self, tag, img_chw, step=None): |
| if step is None: |
| step = self.step |
| loggable = step == 0 or (step + 1) % 500 == 0 |
| else: |
| loggable = True |
| if loggable: |
| self.writer.add_image(tag, img_chw, step, dataformats='CHW') |
| |
| def flush(self): |
| self.writer.flush() |
| |
| def close(self): |
| self.writer.close() |
|
|
|
|
| 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=30, 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! |
| """ |
| t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') |
| tdist.barrier() |
| tdist.all_reduce(t) |
| t = t.tolist() |
| self.count = int(t[0]) |
| self.total = t[1] |
| |
| @property |
| def median(self): |
| return np.median(self.deque) if len(self.deque) else 0 |
| |
| @property |
| def avg(self): |
| return sum(self.deque) / (len(self.deque) or 1) |
| |
| @property |
| def global_avg(self): |
| return self.total / (self.count or 1) |
| |
| @property |
| def max(self): |
| return max(self.deque) |
| |
| @property |
| def value(self): |
| return self.deque[-1] if len(self.deque) else 0 |
| |
| def time_preds(self, counts) -> Tuple[float, str, str]: |
| remain_secs = counts * self.median |
| return remain_secs, str(datetime.timedelta(seconds=round(remain_secs))), time.strftime("%Y-%m-%d %H:%M", time.localtime(time.time() + remain_secs)) |
| |
| 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=' '): |
| self.meters = defaultdict(SmoothedValue) |
| self.delimiter = delimiter |
| self.iter_end_t = time.time() |
| self.log_iters = [] |
| |
| def update(self, **kwargs): |
| for k, v in kwargs.items(): |
| if v is None: |
| continue |
| if hasattr(v, 'item'): 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 len(meter.deque): |
| 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, start_it, max_iters, itrt, print_freq, header=None): |
| self.log_iters = set(np.linspace(0, max_iters-1, print_freq, dtype=int).tolist()) |
| self.log_iters.add(start_it) |
| if not header: |
| header = '' |
| start_time = time.time() |
| self.iter_end_t = time.time() |
| self.iter_time = SmoothedValue(fmt='{avg:.4f}') |
| self.data_time = SmoothedValue(fmt='{avg:.4f}') |
| space_fmt = ':' + str(len(str(max_iters))) + 'd' |
| log_msg = [ |
| header, |
| '[{0' + space_fmt + '}/{1}]', |
| 'eta: {eta}', |
| '{meters}', |
| 'time: {time}', |
| 'data: {data}' |
| ] |
| log_msg = self.delimiter.join(log_msg) |
| |
| if isinstance(itrt, Iterator) and not hasattr(itrt, 'preload') and not hasattr(itrt, 'set_epoch'): |
| for i in range(start_it, max_iters): |
| obj = next(itrt) |
| self.data_time.update(time.time() - self.iter_end_t) |
| yield i, obj |
| self.iter_time.update(time.time() - self.iter_end_t) |
| if i in self.log_iters: |
| eta_seconds = self.iter_time.global_avg * (max_iters - i) |
| eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
| print(log_msg.format( |
| i, max_iters, eta=eta_string, |
| meters=str(self), |
| time=str(self.iter_time), data=str(self.data_time)), flush=True) |
| self.iter_end_t = time.time() |
| else: |
| if isinstance(itrt, int): itrt = range(itrt) |
| for i, obj in enumerate(itrt): |
| self.data_time.update(time.time() - self.iter_end_t) |
| yield i, obj |
| self.iter_time.update(time.time() - self.iter_end_t) |
| if i in self.log_iters: |
| eta_seconds = self.iter_time.global_avg * (max_iters - i) |
| eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
| print(log_msg.format( |
| i, max_iters, eta=eta_string, |
| meters=str(self), |
| time=str(self.iter_time), data=str(self.data_time)), flush=True) |
| self.iter_end_t = time.time() |
| |
| total_time = time.time() - start_time |
| total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
| print('{} Total time: {} ({:.3f} s / it)'.format( |
| header, total_time_str, total_time / max_iters), flush=True) |
|
|
|
|
| def glob_with_latest_modified_first(pattern, recursive=False): |
| return sorted(glob.glob(pattern, recursive=recursive), key=os.path.getmtime, reverse=True) |
|
|
|
|
| def auto_resume(args: arg_util.Args, pattern='ckpt*.pth') -> Tuple[List[str], int, int, dict, dict]: |
| info = [] |
| file = os.path.join(args.local_out_dir_path, pattern) |
| all_ckpt = glob_with_latest_modified_first(file) |
| if len(all_ckpt) == 0: |
| info.append(f'[auto_resume] no ckpt found @ {file}') |
| info.append(f'[auto_resume quit]') |
| return info, 0, 0, {}, {} |
| else: |
| info.append(f'[auto_resume] load ckpt from @ {all_ckpt[0]} ...') |
| ckpt = torch.load(all_ckpt[0], map_location='cpu') |
| ep, it = ckpt['epoch'], ckpt['iter'] |
| info.append(f'[auto_resume success] resume from ep{ep}, it{it}') |
| return info, ep, it, ckpt['trainer'], ckpt['args'] |
|
|
|
|
| def create_npz_from_sample_folder(sample_folder: str): |
| """ |
| Builds a single .npz file from a folder of .png samples. Refer to DiT. |
| """ |
| import os, glob |
| import numpy as np |
| from tqdm import tqdm |
| from PIL import Image |
| |
| samples = [] |
| pngs = glob.glob(os.path.join(sample_folder, '*.png')) + glob.glob(os.path.join(sample_folder, '*.PNG')) |
| assert len(pngs) == 50_000, f'{len(pngs)} png files found in {sample_folder}, but expected 50,000' |
| for png in tqdm(pngs, desc='Building .npz file from samples (png only)'): |
| with Image.open(png) as sample_pil: |
| sample_np = np.asarray(sample_pil).astype(np.uint8) |
| samples.append(sample_np) |
| samples = np.stack(samples) |
| assert samples.shape == (50_000, samples.shape[1], samples.shape[2], 3) |
| npz_path = f'{sample_folder}.npz' |
| np.savez(npz_path, arr_0=samples) |
| print(f'Saved .npz file to {npz_path} [shape={samples.shape}].') |
| return npz_path |
|
|
