import argparse, os, sys, datetime, glob, importlib, csv import numpy as np import time import torch import torchvision import pytorch_lightning as pl from omegaconf import OmegaConf from PIL import Image from torchvision.utils import make_grid from einops import rearrange from logging import Logger from typing import Callable, Optional from pytorch_lightning.callbacks import Callback from pytorch_lightning.utilities import rank_zero_only from pytorch_lightning.utilities import rank_zero_info from vtdm.util import tensor2vid, export_to_video class SetupCallback(Callback): def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): super().__init__() self.resume = resume self.now = now self.logdir = logdir self.ckptdir = ckptdir self.cfgdir = cfgdir self.config = config self.lightning_config = lightning_config def on_keyboard_interrupt(self, trainer, pl_module): if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(self.ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def on_pretrain_routine_start(self, trainer, pl_module): #def on_fit_start(self, trainer, pl_module): if trainer.global_rank == 0: # Create logdirs and save configs os.makedirs(self.logdir, exist_ok=True) os.makedirs(self.ckptdir, exist_ok=True) os.makedirs(self.cfgdir, exist_ok=True) if "callbacks" in self.lightning_config: if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']: os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) print("Project config") print(OmegaConf.to_yaml(self.config)) OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) print("Lightning config") print(OmegaConf.to_yaml(self.lightning_config)) OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) else: # ModelCheckpoint callback created log directory --- remove it if not self.resume and os.path.exists(self.logdir): dst, name = os.path.split(self.logdir) dst = os.path.join(dst, "child_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) # try: # os.rename(self.logdir, dst) # except FileNotFoundError: # pass class ImageLogger(Callback): def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps=True, rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False, log_images_kwargs=None): super().__init__() self.rescale = rescale self.batch_freq = batch_frequency self.max_images = max_images if not increase_log_steps: self.log_steps = [self.batch_freq] self.clamp = clamp self.disabled = disabled self.log_on_batch_idx = log_on_batch_idx self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} self.log_first_step = log_first_step @rank_zero_only def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx): root = os.path.join(save_dir, "image_log", split) for k in images: filename = "{}_gs-{:06}_e-{:06}_b-{:06}".format(k, global_step, current_epoch, batch_idx) path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) if 'video' in k: # log to video export_to_video(images[k], path + '.mp4', save_to_gif=False, use_cv2=False, fps=6) else: grid = torchvision.utils.make_grid(images[k], nrow=4) if self.rescale: grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) grid = grid.numpy() grid = (grid * 255).astype(np.uint8) Image.fromarray(grid).save(path + '.png') def log_img(self, pl_module, batch, batch_idx, split="train"): check_idx = batch_idx # if self.log_on_batch_idx else pl_module.global_step if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0 hasattr(pl_module, "log_images") and callable(pl_module.log_images) and self.max_images > 0): logger = type(pl_module.logger) is_train = pl_module.training if is_train: pl_module.eval() with torch.no_grad(): images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) for k in images: if 'video' in k: # log to video images[k] = tensor2vid(images[k]) else: images[k] = images[k].to(dtype=torch.float32) N = min(images[k].shape[0], self.max_images) images[k] = images[k][:N] if isinstance(images[k], torch.Tensor): images[k] = images[k].detach().cpu() if self.clamp: images[k] = torch.clamp(images[k], -1., 1.) self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch, batch_idx) if is_train: pl_module.train() def check_frequency(self, check_idx): return check_idx % self.batch_freq == 0 def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): if not self.disabled: self.log_img(pl_module, batch, batch_idx, split="train") class CUDACallback(Callback): # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py def on_train_epoch_start(self, trainer, pl_module): # Reset the memory use counter torch.cuda.reset_peak_memory_stats(trainer.root_gpu) torch.cuda.synchronize(trainer.root_gpu) self.start_time = time.time() def on_train_epoch_end(self, trainer, pl_module): torch.cuda.synchronize(trainer.root_gpu) max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20 epoch_time = time.time() - self.start_time try: max_memory = trainer.training_type_plugin.reduce(max_memory) epoch_time = trainer.training_type_plugin.reduce(epoch_time) rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds") rank_zero_info(f"Average Peak memory {max_memory:.2f} MiB") except AttributeError: pass class TextProgressBar(pl.callbacks.ProgressBarBase): """A custom ProgressBar to log the training progress.""" def __init__(self, logger: Logger, refresh_rate: int = 50) -> None: super().__init__() self._logger = logger self._refresh_rate = refresh_rate self._enabled = True # a time flag to indicate the beginning of an epoch self._time = 0 @property def refresh_rate(self) -> int: return self._refresh_rate @property def is_enabled(self) -> bool: return self._enabled @property def is_disabled(self) -> bool: return not self.is_enabled def disable(self) -> None: # No need to disable the ProgressBar on processes with LOCAL_RANK != 1, because the # StreamHandler of logging is disabled on these processes. self._enabled = True def enable(self) -> None: self._enabled = True @staticmethod def _serialize_metrics(progressbar_log_dict: dict, filter_fn: Optional[Callable[[str], bool]] = None) -> str: if filter_fn: progressbar_log_dict = {k: v for k, v in progressbar_log_dict.items() if filter_fn(k)} msg = '' for metric, value in progressbar_log_dict.items(): if type(value) is str: msg += f'{metric}: {value:.5f} ' elif 'acc' in metric: msg += f'{metric}: {value:.3%} ' else: msg += f'{metric}: {value:f} ' return msg def on_train_start(self, trainer, pl_module): super().on_train_start(trainer, pl_module) def on_train_epoch_start(self, trainer, pl_module): super().on_train_epoch_start(trainer, pl_module) self._logger.info(f'Epoch: {trainer.current_epoch}, batch_num: {self.total_train_batches}') self._time = time.time() def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): # super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx) current = self.train_batch_idx if self._should_update(current, self.total_train_batches): batch_time = (time.time() - self._time) / self.train_batch_idx msg = f'[Epoch {trainer.current_epoch}] [Batch {self.train_batch_idx}/{self.total_train_batches} {batch_time:.2f} s/batch] => ' if current != self.total_train_batches: filter_fn = lambda x: not x.startswith('val') and not x.startswith('test') and not x.startswith('global') and not x.endswith('_epoch') else: filter_fn = lambda x: not x.startswith('val') and not x.startswith('test') and not x.startswith('global') msg += self._serialize_metrics(trainer.progress_bar_metrics, filter_fn=filter_fn) self._logger.info(msg) def on_train_end(self, trainer, pl_module): super().on_train_end(trainer, pl_module) self._logger.info(f'Training finished.') def on_validation_start(self, trainer, pl_module): super().on_validation_start(trainer, pl_module) self._logger.info('Validation Begins. Epoch: {}, val batch num: {}'.format(trainer.current_epoch, self.total_val_batches)) def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) current = self.val_batch_idx if self._should_update(current, self.total_val_batches): batch_time = (time.time() - self._time) / self.val_batch_idx msg = f'[Epoch {trainer.current_epoch}] [Val Batch {self.val_batch_idx}/{self.total_val_batches} {batch_time:.2f} s/batch] => ' if current != self.total_val_batches: filter_fn = lambda x: x.startswith('val') and not x.endswith('_epoch') else: filter_fn = lambda x: x.startswith('val') msg += self._serialize_metrics(trainer.progress_bar_metrics, filter_fn=filter_fn) self._logger.info(msg) def on_validation_end(self, trainer, pl_module): super().on_validation_end(trainer, pl_module) msg = f'[Epoch {trainer.current_epoch}] [Validation finished] => ' msg += self._serialize_metrics(trainer.progress_bar_metrics, filter_fn=lambda x: x.startswith('val') and x.endswith('_epoch')) self._logger.info(msg) def get_metrics(self, trainer, pl_module): items = super().get_metrics(trainer, pl_module) # don't show the version number items.pop("v_num", None) return items def _should_update(self, current: int, total: int) -> bool: return self.is_enabled and (current % self.refresh_rate == 0 or current == total) def print(self, *args, sep: str = " ", **kwargs): s = sep.join(map(str, args)) self._logger.info(f"[Progress Print] {s}")