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import os
import numpy as np
from PIL import Image
import torch
import torchvision
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities.distributed import rank_zero_only
from vq_gan_3d.utils import save_video_grid
class ImageLogger(Callback):
def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True):
super().__init__()
self.batch_freq = batch_frequency
self.max_images = max_images
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
@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:
images[k] = (images[k] + 1.0) * 127.5 # std + mean
torch.clamp(images[k], 0, 255)
grid = torchvision.utils.make_grid(images[k], nrow=4)
grid = grid
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid).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"):
if (self.check_frequency(batch_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)
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()
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, batch_idx):
if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps):
try:
self.log_steps.pop(0)
except IndexError:
pass
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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):
self.log_img(pl_module, batch, batch_idx, split="val")
class VideoLogger(Callback):
def __init__(self, batch_frequency, max_videos, clamp=True, increase_log_steps=True):
super().__init__()
self.batch_freq = batch_frequency
self.max_videos = max_videos
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
@rank_zero_only
def log_local(self, save_dir, split, videos,
global_step, current_epoch, batch_idx):
root = os.path.join(save_dir, "videos", split)
for k in videos:
videos[k] = (videos[k] + 1.0) * 127.5 # std + mean
torch.clamp(videos[k], 0, 255)
videos[k] = videos[k] / 255.0
grid = videos[k]
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.mp4".format(
k,
global_step,
current_epoch,
batch_idx)
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
save_video_grid(grid, path)
def log_vid(self, pl_module, batch, batch_idx, split="train"):
if (self.check_frequency(batch_idx) and
hasattr(pl_module, "log_videos") and
callable(pl_module.log_videos) and
self.max_videos > 0):
logger = type(pl_module.logger)
is_train = pl_module.training
if is_train:
pl_module.eval()
with torch.no_grad():
videos = pl_module.log_videos(
batch, split=split, batch_idx=batch_idx)
for k in videos:
N = min(videos[k].shape[0], self.max_videos)
videos[k] = videos[k][:N]
if isinstance(videos[k], torch.Tensor):
videos[k] = videos[k].detach().cpu()
self.log_local(pl_module.logger.save_dir, split, videos,
pl_module.global_step, pl_module.current_epoch, batch_idx)
if is_train:
pl_module.train()
def check_frequency(self, batch_idx):
if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps):
try:
self.log_steps.pop(0)
except IndexError:
pass
return True
return False
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self.log_vid(pl_module, batch, batch_idx, split="train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self.log_vid(pl_module, batch, batch_idx, split="val")
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