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polish code (#3194) 

Co-authored-by: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com>
pull/3241/head
NatalieC323 2023-03-24 18:44:43 +08:00 committed by GitHub
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37
examples/images/diffusion/README.md
View File

@ -47,40 +47,21 @@ conda env create -f environment.yaml
conda activate ldm
```
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running:
```
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install transformers diffusers invisible-watermark
```
#### Step 2: install lightning
Install Lightning version later than 2022.01.04. We suggest you install lightning from source. Notice that the default download path of pip should be within the conda environment, or you may need to specify using 'which pip' and redirect the path into conda environment.
##### From Source
```
git clone https://github.com/Lightning-AI/lightning.git
pip install -r requirements.txt
python setup.py install
```
##### From pip
```
pip install pytorch-lightning
```
#### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website
#### Step 2:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website
You can install the latest version (0.2.7) from our official website or from source. Notice that the suitable version for this training is colossalai(0.2.5), which stands for torch(1.12.1).
##### Download suggested verision for this training
```
pip install colossalai==0.2.5
```
##### Download the latest version from pip for latest torch version
@ -89,7 +70,7 @@ pip install colossalai==0.2.5
pip install colossalai
```
##### From source
##### From source:
```
git clone https://github.com/hpcaitech/ColossalAI.git
@ -99,7 +80,7 @@ cd ColossalAI
CUDA_EXT=1 pip install .
```
#### Step 4:Accelerate with flash attention by xformers(Optional)
#### Step 3:Accelerate with flash attention by xformers(Optional)
Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.12.0. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/)
@ -113,7 +94,7 @@ To use the stable diffusion Docker image, you can either build using the provide
```
# 1. build from dockerfile
cd docker
cd ColossalAI/examples/images/diffusion/docker
docker build -t hpcaitech/diffusion:0.2.0 .
# 2. pull from our docker hub
@ -127,7 +108,7 @@ Once you have the image ready, you can launch the image with the following comma
# On Your Host Machine #
########################
# make sure you start your image in the repository root directory
cd Colossal-AI
cd ColossalAI
# run the docker container
docker run --rm \
@ -144,13 +125,15 @@ docker run --rm \
# Once you have entered the docker container, go to the stable diffusion directory for training
cd examples/images/diffusion/
# Download the model checkpoint from pretrained (See the following steps)
# Set up your configuration the "train_colossalai.sh" (See the following steps)
# start training with colossalai
bash train_colossalai.sh
```
It is important for you to configure your volume mapping in order to get the best training experience.
1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v <your-data-dir>:/data/scratch`, where you need to replace `<your-data-dir>` with the actual data path on your machine.
2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v <hf-cache-dir>:/root/.cache/huggingface`, where you need to repliace the `<hf-cache-dir>` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`.
1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v <your-data-dir>:/data/scratch`, where you need to replace `<your-data-dir>` with the actual data path on your machine. Notice that within docker we need to transform Win expresison into Linuxd, e.g. C:\User\Desktop into /c/User/Desktop.
2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v <hf-cache-dir>:/root/.cache/huggingface`, where you need to replace the `<hf-cache-dir>` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`.
3. **Optional**, if you encounter any problem stating that shared memory is insufficient inside container, please add `-v /dev/shm:/dev/shm` to your `docker run` command.

74
examples/images/diffusion/ldm/data/lsun.py
View File

@ -5,87 +5,105 @@ from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
# This class is used to create a dataset of images from LSUN dataset for training
class LSUNBase(Dataset):
def __init__(self,
txt_file,
data_root,
size=None,
interpolation="bicubic",
flip_p=0.5
txt_file, # path to the text file containing the list of image paths
data_root, # root directory of the LSUN dataset
size=None, # the size of images to resize to
interpolation="bicubic", # interpolation method to be used while resizing
flip_p=0.5 # probability of random horizontal flipping
):
self.data_paths = txt_file
self.data_root = data_root
with open(self.data_paths, "r") as f:
self.image_paths = f.read().splitlines()
self._length = len(self.image_paths)
self.data_paths = txt_file # store path to text file containing list of images
self.data_root = data_root # store path to root directory of the dataset
with open(self.data_paths, "r") as f: # open and read the text file
self.image_paths = f.read().splitlines() # read the lines of the file and store as list
self._length = len(self.image_paths) # store the number of images
# create dictionary to hold image path information
self.labels = {
"relative_file_path_": [l for l in self.image_paths],
"file_path_": [os.path.join(self.data_root, l)
for l in self.image_paths],
}
self.size = size
# set the image size to be resized
self.size = size
# set the interpolation method for resizing the image
self.interpolation = {"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
}[interpolation]
# randomly flip the image horizontally with a given probability
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
# return the length of dataset
return self._length
def __getitem__(self, i):
# get the image path for the given index
example = dict((k, self.labels[k][i]) for k in self.labels)
image = Image.open(example["file_path_"])
# convert it to RGB format
if not image.mode == "RGB":
image = image.convert("RGB")
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
crop = min(img.shape[0], img.shape[1])
h, w, = img.shape[0], img.shape[1]
img = np.array(image).astype(np.uint8) # convert image to numpy array
crop = min(img.shape[0], img.shape[1]) # crop the image to a square shape
h, w, = img.shape[0], img.shape[1] # get the height and width of image
img = img[(h - crop) // 2:(h + crop) // 2,
(w - crop) // 2:(w + crop) // 2]
(w - crop) // 2:(w + crop) // 2] # crop the image to a square shape
image = Image.fromarray(img)
if self.size is not None:
image = Image.fromarray(img) # create an image from numpy array
if self.size is not None: # if image size is provided, resize the image
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip(image)
image = np.array(image).astype(np.uint8)
example["image"] = (image / 127.5 - 1.0).astype(np.float32)
return example
image = self.flip(image) # flip the image horizontally with the given probability
image = np.array(image).astype(np.uint8)
example["image"] = (image / 127.5 - 1.0).astype(np.float32) # normalize the image values and convert to float32
return example # return the example dictionary containing the image and its file paths
#A dataset class for LSUN Churches training set.
# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments.
# The text file containing the paths to the images and the root directory where the images are stored are passed as arguments. Any additional keyword arguments passed to this class will be forwarded to the constructor of the parent class.
class LSUNChurchesTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
#A dataset class for LSUN Churches validation set.
# It is similar to LSUNChurchesTrain except that it uses a different text file and sets the flip probability to zero by default.
class LSUNChurchesValidation(LSUNBase):
def __init__(self, flip_p=0., **kwargs):
super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
flip_p=flip_p, **kwargs)
# A dataset class for LSUN Bedrooms training set.
# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments.
class LSUNBedroomsTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
# A dataset class for LSUN Bedrooms validation set.
# It is similar to LSUNBedroomsTrain except that it uses a different text file and sets the flip probability to zero by default.
class LSUNBedroomsValidation(LSUNBase):
def __init__(self, flip_p=0.0, **kwargs):
super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
flip_p=flip_p, **kwargs)
# A dataset class for LSUN Cats training set.
# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments.
# The text file containing the paths to the images and the root directory where the images are stored are passed as arguments.
class LSUNCatsTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
# A dataset class for LSUN Cats validation set.
# It is similar to LSUNCatsTrain except that it uses a different text file and sets the flip probability to zero by default.
class LSUNCatsValidation(LSUNBase):
def __init__(self, flip_p=0., **kwargs):
super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",

178
examples/images/diffusion/main.py
View File

@ -44,14 +44,18 @@ from ldm.util import instantiate_from_config
class DataLoaderX(DataLoader):
# A custom data loader class that inherits from DataLoader
def __iter__(self):
# Overriding the __iter__ method of DataLoader to return a BackgroundGenerator
#This is to enable data laoding in the background to improve training performance
return BackgroundGenerator(super().__iter__())
def get_parser(**parser_kwargs):
#A function to create an ArgumentParser object and add arguments to it
def str2bool(v):
# A helper function to parse boolean values from command line arguments
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
@ -60,8 +64,10 @@ def get_parser(**parser_kwargs):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
# Create an ArgumentParser object with specifies kwargs
parser = argparse.ArgumentParser(**parser_kwargs)
# Add vairous command line arguments with their default balues and descriptions
parser.add_argument(
"-n",
"--name",
@ -161,14 +167,18 @@ def get_parser(**parser_kwargs):
return parser
# A function that returns the non-default arguments between two objects
def nondefault_trainer_args(opt):
# create an argument parsser
parser = argparse.ArgumentParser()
# add pytorch lightning trainer default arguments
parser = Trainer.add_argparse_args(parser)
# parse the empty arguments to obtain the default values
args = parser.parse_args([])
# return all non-default arguments
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
# A dataset wrapper class to create a pytorch dataset from an arbitrary object
class WrappedDataset(Dataset):
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
@ -181,7 +191,7 @@ class WrappedDataset(Dataset):
def __getitem__(self, idx):
return self.data[idx]
# A function to initialize worker processes
def worker_init_fn(_):
worker_info = torch.utils.data.get_worker_info()
@ -189,15 +199,18 @@ def worker_init_fn(_):
worker_id = worker_info.id
if isinstance(dataset, Txt2ImgIterableBaseDataset):
#divide the dataset into equal parts for each worker
split_size = dataset.num_records // worker_info.num_workers
#set the sample IDs for the current worker
# reset num_records to the true number to retain reliable length information
dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
# set the seed for the current worker
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
else:
return np.random.seed(np.random.get_state()[1][0] + worker_id)
#Provide functionality for creating data loadedrs based on provided dataset configurations
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(self,
@ -212,10 +225,12 @@ class DataModuleFromConfig(pl.LightningDataModule):
use_worker_init_fn=False,
shuffle_val_dataloader=False):
super().__init__()
# Set data module attributes
self.batch_size = batch_size
self.dataset_configs = dict()
self.num_workers = num_workers if num_workers is not None else batch_size * 2
self.use_worker_init_fn = use_worker_init_fn
# If a dataset is passed, add it to the dataset configs and create a corresponding dataloader method
if train is not None:
self.dataset_configs["train"] = train
self.train_dataloader = self._train_dataloader
@ -231,21 +246,28 @@ class DataModuleFromConfig(pl.LightningDataModule):
self.wrap = wrap
def prepare_data(self):
# Instantiate datasets
for data_cfg in self.dataset_configs.values():
instantiate_from_config(data_cfg)
def setup(self, stage=None):
# Instantiate datasets from the dataset configs
self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
# If wrap is true, create a WrappedDataset for each dataset
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_dataloader(self):
#Check if the train dataset is iterable
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
#Set the worker initialization function of the dataset isiterable or use_worker_init_fn is True
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# Return a DataLoaderX object for the train dataset
return DataLoaderX(self.datasets["train"],
batch_size=self.batch_size,
num_workers=self.num_workers,
@ -253,10 +275,12 @@ class DataModuleFromConfig(pl.LightningDataModule):
worker_init_fn=init_fn)
def _val_dataloader(self, shuffle=False):
#Check if the validation dataset is iterable
if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
# Return a DataLoaderX object for the validation dataset
return DataLoaderX(self.datasets["validation"],
batch_size=self.batch_size,
num_workers=self.num_workers,
@ -264,7 +288,9 @@ class DataModuleFromConfig(pl.LightningDataModule):
shuffle=shuffle)
def _test_dataloader(self, shuffle=False):
# Check if the test dataset is iterable
is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
# Set the worker initialization function if the dataset is iterable or use_worker_init_fn is True
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
@ -291,6 +317,7 @@ class DataModuleFromConfig(pl.LightningDataModule):
class SetupCallback(Callback):
# I nitialize the callback with the necessary parameters
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
super().__init__()
@ -302,12 +329,14 @@ class SetupCallback(Callback):
self.config = config
self.lightning_config = lightning_config
# Save a checkpoint if training is interrupted with keyboard interrupt
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)
# Create necessary directories and save configuration files before training starts
# def on_pretrain_routine_start(self, trainer, pl_module):
def on_fit_start(self, trainer, pl_module):
if trainer.global_rank == 0:
@ -316,6 +345,7 @@ class SetupCallback(Callback):
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
#Create trainstep checkpoint directory if necessary
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)
@ -323,11 +353,13 @@ class SetupCallback(Callback):
print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
# Save project config and lightning config as YAML files
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)))
# Remove log directory if resuming training and directory already exists
else:
# ModelCheckpoint callback created log directory --- remove it
if not self.resume and os.path.exists(self.logdir):
@ -346,25 +378,28 @@ class SetupCallback(Callback):
# trainer.save_checkpoint(ckpt_path)
# PyTorch Lightning callback for ogging images during training and validation of a deep learning model
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):
batch_frequency, # Frequency of batches on which to log images
max_images, # Maximum number of images to log
clamp=True, # Whether to clamp pixel values to [-1,1]
increase_log_steps=True, # Whether to increase frequency of log steps exponentially
rescale=True, # Whetehr to rescale pixel values to [0,1]
disabled=False, # Whether to disable logging
log_on_batch_idx=False, # Whether to log on baych index instead of global step
log_first_step=False, # Whetehr to log on the first step
log_images_kwargs=None): # Additional keyword arguments to pass to log_images method
super().__init__()
self.rescale = rescale
self.batch_freq = batch_frequency
self.max_images = max_images
self.logger_log_images = {
pl.loggers.CSVLogger: self._testtube,
# Dictionary of logger classes and their corresponding logging methods
pl.loggers.CSVLogger: self._testtube,
}
# Create a list of exponentially increasing log steps, starting from 1 and ending at batch_frequency
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]
@ -374,17 +409,32 @@ class ImageLogger(Callback):
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):
@rank_zero_only # Ensure that only the first process in distributed training executes this method
def _testtube(self, # The PyTorch Lightning module
pl_module, # A dictionary of images to log.
images, #
batch_idx, # The batch index.
split # The split (train/val) on which to log the images
):
# Method for logging images using test-tube logger
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}"
# Add image grid to logger's experiment
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):
def log_local(self,
save_dir,
split, # The split (train/val) on which to log the images
images, # A dictionary of images to log
global_step, # The global step
current_epoch, # The current epoch.
batch_idx
):
# Method for saving image grids to local file system
root = os.path.join(save_dir, "images", split)
for k in images:
grid = torchvision.utils.make_grid(images[k], nrow=4)
@ -396,12 +446,16 @@ class ImageLogger(Callback):
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)
# Save image grid as PNG file
Image.fromarray(grid).save(path)
def log_img(self, pl_module, batch, batch_idx, split="train"):
#Function for logging images to both the logger and local file system.
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
# check if it's time to log an image batch
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):
# Get logger type and check if training mode is on
logger = type(pl_module.logger)
is_train = pl_module.training
@ -409,8 +463,10 @@ class ImageLogger(Callback):
pl_module.eval()
with torch.no_grad():
# Get images from log_images method of the pl_module
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
# Clip images if specified and convert to CPU tensor
for k in images:
N = min(images[k].shape[0], self.max_images)
images[k] = images[k][:N]
@ -419,15 +475,19 @@ class ImageLogger(Callback):
if self.clamp:
images[k] = torch.clamp(images[k], -1., 1.)
# Log images locally to file system
self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch,
batch_idx)
# log the images using the logger
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
logger_log_images(pl_module, images, pl_module.global_step, split)
# switch back to training mode if necessary
if is_train:
pl_module.train()
# The function checks if it's time to log an image batch
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):
@ -439,14 +499,17 @@ class ImageLogger(Callback):
return True
return False
# Log images on train batch end if logging is not disabled
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_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")
pass
# Log images on validation batch end if logging is not disabled and in validation mode
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
if not self.disabled and pl_module.global_step > 0:
self.log_img(pl_module, batch, batch_idx, split="val")
# log gradients during calibration if necessary
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)
@ -458,6 +521,7 @@ class CUDACallback(Callback):
def on_train_start(self, trainer, pl_module):
rank_zero_info("Training is starting")
#the method is called at the end of each training epoch
def on_train_end(self, trainer, pl_module):
rank_zero_info("Training is ending")
@ -524,6 +588,7 @@ if __name__ == "__main__":
# params:
# key: value
# get the current time to create a new logging directory
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
@ -535,11 +600,13 @@ if __name__ == "__main__":
parser = Trainer.add_argparse_args(parser)
opt, unknown = parser.parse_known_args()
# Veirfy the arguments are both specified
if opt.name and opt.resume:
raise ValueError("-n/--name and -r/--resume cannot be specified both."
"If you want to resume training in a new log folder, "
"use -n/--name in combination with --resume_from_checkpoint")
# Check if the "resume" option is specified, resume training from the checkpoint if it is true
ckpt = None
if opt.resume:
rank_zero_info("Resuming from {}".format(opt.resume))
@ -557,8 +624,10 @@ if __name__ == "__main__":
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
# Finds all ".yaml" configuration files in the log directory and adds them to the list of base configurations
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
# Gets the name of the current log directory by splitting the path and taking the last element.
_tmp = logdir.split("/")
nowname = _tmp[-1]
else:
@ -574,13 +643,17 @@ if __name__ == "__main__":
nowname = now + name + opt.postfix
logdir = os.path.join(opt.logdir, nowname)
# Sets the checkpoint path of the 'ckpt' option is specified
if opt.ckpt:
ckpt = opt.ckpt
# Create the checkpoint and configuration directories within the log directory.
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
# Sets the seed for the random number generator to ensure reproducibility
seed_everything(opt.seed)
# Intinalize and save configuratioon using teh OmegaConf library.
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
@ -593,6 +666,7 @@ if __name__ == "__main__":
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
# Check whether the accelerator is gpu
if not trainer_config["accelerator"] == "gpu":
del trainer_config["accelerator"]
cpu = True
@ -609,6 +683,7 @@ if __name__ == "__main__":
config.model["params"].update({"use_fp16": False})
if ckpt is not None:
#If a checkpoint path is specified in the ckpt variable, the code updates the "ckpt" key in the "params" dictionary of the config.model configuration with the value of ckpt
config.model["params"].update({"ckpt": ckpt})
rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"]))
@ -617,7 +692,8 @@ if __name__ == "__main__":
trainer_kwargs = dict()
# config the logger
# default logger configs
# Default logger configs to log training metrics during the training process.
# These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger.
default_logger_cfgs = {
"wandb": {
"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
@ -638,6 +714,7 @@ if __name__ == "__main__":
}
}
# Set up the logger for TensorBoard
default_logger_cfg = default_logger_cfgs["tensorboard"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
@ -660,6 +737,7 @@ if __name__ == "__main__":
trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
# Set up ModelCheckpoint callback to save best models
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
@ -683,45 +761,50 @@ if __name__ == "__main__":
if version.parse(pl.__version__) < version.parse('1.4.0'):
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
# Set up various callbacks, including logging, learning rate monitoring, and CUDA management
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": {
"setup_callback": { # callback to set up the training
"target": "main.SetupCallback",
"params": {
"resume": opt.resume,
"now": now,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
"lightning_config": lightning_config,
"resume": opt.resume, # resume training if applicable
"now": now,
"logdir": logdir, # directory to save the log file
"ckptdir": ckptdir, # directory to save the checkpoint file
"cfgdir": cfgdir, # directory to save the configuration file
"config": config, # configuration dictionary
"lightning_config": lightning_config, # LightningModule configuration
}
},
"image_logger": {
"image_logger": { # callback to log image data
"target": "main.ImageLogger",
"params": {
"batch_frequency": 750,
"max_images": 4,
"clamp": True
"batch_frequency": 750, # how frequently to log images
"max_images": 4, # maximum number of images to log
"clamp": True # whether to clamp pixel values to [0,1]
}
},
"learning_rate_logger": {
"learning_rate_logger": { # callback to log learning rate
"target": "main.LearningRateMonitor",
"params": {
"logging_interval": "step",
# "log_momentum": True
"logging_interval": "step", # logging frequency (either 'step' or 'epoch')
# "log_momentum": True # whether to log momentum (currently commented out)
}
},
"cuda_callback": {
"cuda_callback": { # callback to handle CUDA-related operations
"target": "main.CUDACallback"
},
}
# If the LightningModule configuration has specified callbacks, use those
# Otherwise, create an empty OmegaConf configuration object
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
# If the 'metrics_over_trainsteps_checkpoint' callback is specified in the
# LightningModule configuration, update the default callbacks configuration
if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
print(
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
@ -739,15 +822,17 @@ if __name__ == "__main__":
}
}
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
# Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
# Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir
# data
# Create a data module based on the configuration file
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
@ -755,10 +840,12 @@ if __name__ == "__main__":
data.prepare_data()
data.setup()
# Print some information about the datasets in the data module
for k in data.datasets:
rank_zero_info(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
# configure learning rate
# Configure learning rate based on the batch size, base learning rate and number of GPUs
# If scale_lr is true, calculate the learning rate based on additional factors
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
if not cpu:
ngpu = trainer_config["devices"]
@ -780,7 +867,7 @@ if __name__ == "__main__":
rank_zero_info("++++ NOT USING LR SCALING ++++")
rank_zero_info(f"Setting learning rate to {model.learning_rate:.2e}")
# allow checkpointing via USR1
# Allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
@ -794,20 +881,23 @@ if __name__ == "__main__":
pudb.set_trace()
import signal
# Assign melk to SIGUSR1 signal and divein to SIGUSR2 signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# run
# Run the training and validation
if opt.train:
try:
trainer.fit(model, data)
except Exception:
melk()
raise
# Print the maximum GPU memory allocated during training
print(f"GPU memory usage: {torch.cuda.max_memory_allocated() / 1024**2:.0f} MB")
# if not opt.no_test and not trainer.interrupted:
# trainer.test(model, data)
except Exception:
# If there's an exception, debug it if opt.debug is true and the trainer's global rank is 0
if opt.debug and trainer.global_rank == 0:
try:
import pudb as debugger
@ -816,7 +906,7 @@ if __name__ == "__main__":
debugger.post_mortem()
raise
finally:
# move newly created debug project to debug_runs
# Move the log directory to debug_runs if opt.debug is true and the trainer's global
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
dst = os.path.join(dst, "debug_runs", name)