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import os
import numpy as np
import torch
import torchvision
from PIL import Image
from pytorch_lightning.callbacks import Callback
import pytorch_lightning as pl
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import OmegaConf
# 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:
# 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)
# filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
# path = os.path.join(root, filename)
# os.makedirs(os.path.split(path)[0], exist_ok=True)
# Image.fromarray(grid).save(path)
# 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:
# 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 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):
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, max_images, 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
self.logger_log_images = {
pl.loggers.TestTubeLogger: self._testtube,
}
self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
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 _testtube(self, pl_module, images, batch_idx, split):
for k in images:
grid = torchvision.utils.make_grid(images[k])
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
tag = f"{split}/{k}"
pl_module.logger.experiment.add_image(
tag, grid,
global_step=pl_module.global_step)
@rank_zero_only
def log_local(self, save_dir, split, images,
global_step, current_epoch, batch_idx):
root = os.path.join(save_dir, "images", split)
for k in images:
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)
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
k,
global_step,
current_epoch,
batch_idx)
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
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:
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)
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
logger_log_images(pl_module, images, pl_module.global_step, split)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
check_idx > 0 or self.log_first_step):
try:
self.log_steps.pop(0)
except IndexError as e:
print(e)
pass
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
self.log_img(pl_module, batch, batch_idx, split="train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
# if not self.disabled and pl_module.global_step > 0:
# self.log_img(pl_module, batch, batch_idx, split="val")
# if hasattr(pl_module, 'calibrate_grad_norm'):
# if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
# self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
pass |