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}")