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torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.distributed as dist
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision.datasets import ImageFolder
import argparse
import os, yaml
from safetensors.torch import save_file
from datetime import datetime
from datasets.img_latent_dataset import ImgLatentDataset
from tokenizer import models_mae
from tokenizer.sdvae import Diffusers_AutoencoderKL
def load_config(config_path):
with open(config_path, "r") as file:
config = yaml.safe_load(file)
return config
def main(args, train_config):
"""
Run a tokenizer on full dataset and save the features.
"""
assert torch.cuda.is_available(), "Extract features currently requires at least one GPU."
# Setup DDP:
try:
dist.init_process_group("nccl")
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
world_size = dist.get_world_size()
seed = args.seed + rank
if rank == 0:
print(f"Starting rank={rank}, seed={seed}, world_size={world_size}.")
except:
print("Failed to initialize DDP. Running in local mode.")
rank = 0
device = 0
world_size = 1
seed = args.seed
torch.manual_seed(seed)
torch.cuda.set_device(device)
model_name = train_config['vae']['model_name'].split("_")[0]
output_path = os.path.dirname(train_config['data']['origin_path'])
dataset_name = train_config['data']['name']
# Setup feature folders:
output_dir = os.path.join(output_path, f'{model_name}_feature_{dataset_name}_{args.data_split}_{args.image_size}')
if 'sample' in train_config['data']:
output_dir += '_sample'
if rank == 0:
os.makedirs(output_dir, exist_ok=True)
print(model_name)
# Create model:
if model_name == 'vmae':
arch = 'mae_for_ldmae_f8d16_prev'
# chkpt = 'pretrain_weight/mae60_kl_f8d16_200ep.pth'
chkpt = train_config['vae']['weight_path']
tokenizer = getattr(models_mae, arch)(ldmae_mode=True, no_cls=True, kl_loss_weight=True, smooth_output=True, img_size=args.image_size)
checkpoint = torch.load(chkpt, map_location='cpu')
tokenizer = tokenizer.to(device).eval()
msg = tokenizer.load_state_dict(checkpoint['model'], strict=False)
if rank == 0:
print(model_name, msg)
elif model_name in ['ae','dae', 'vae','sdv3']:
tokenizer = Diffusers_AutoencoderKL(
img_size=args.image_size,
sample_size=128,
in_channels=3,
out_channels=3,
layers_per_block=2,
latent_channels=16,
norm_num_groups=32,
act_fn="silu",
block_out_channels=(128, 256, 512, 512),
force_upcast=False,
use_quant_conv=False,
use_post_quant_conv=False,
down_block_types=(
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
),
up_block_types=(
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
),
).to(device).eval()
# chkpt_dir = "./pretrain_weight/sdv3f8d16.pth"
chkpt = train_config['vae']['weight_path']
checkpoint = torch.load(chkpt, map_location='cpu')
msg = tokenizer.load_state_dict(checkpoint['model'], strict=False)
if rank == 0:
print(model_name, msg)
else:
raise("")
print(f"{device} GPU - Model loaded")
# Setup data:
data_path = train_config['data']['origin_path']
datasets = [
ImageFolder(os.path.join(data_path, args.data_split), transform=tokenizer.img_transform(p_hflip=0.0, img_size=args.image_size)),
ImageFolder(os.path.join(data_path, args.data_split), transform=tokenizer.img_transform(p_hflip=1.0, img_size=args.image_size))
]
samplers = [
DistributedSampler(
dataset,
num_replicas=world_size,
rank=rank,
shuffle=False,
seed=args.seed
) for dataset in datasets
] # Maybe gray scale files are dropped. Need to be fixed.
loaders = [
DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=sampler,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False
) for dataset, sampler in zip(datasets, samplers)
]
total_data_in_loop = len(loaders[0].dataset)
if rank == 0:
print(f"Total data in one loop: {total_data_in_loop}")
run_images = 0
saved_files = 0
latents = []
latents_flip = []
labels = []
for batch_idx, batch_data in enumerate(zip(*loaders)):
run_images += batch_data[0][0].shape[0]
if run_images % 100 == 0 and rank == 0:
print(f'{datetime.now()} processing {run_images} of {total_data_in_loop} images')
for loader_idx, data in enumerate(batch_data):
x = data[0].to(device)
y = data[1] # (N,)
with torch.no_grad():
if 'sample' in train_config['data']:
z = tokenizer._encode(x)
else:
z = tokenizer.encode(x).latent_dist.mode().detach().cpu() # (N, C, H, W)
if batch_idx == 0 and rank == 0:
print('latent shape', z.shape, 'dtype', z.dtype)
if loader_idx == 0:
latents.append(z)
labels.append(y)
else:
latents_flip.append(z)
if len(latents) == 10000 // args.batch_size:
latents = torch.cat(latents, dim=0)
latents_flip = torch.cat(latents_flip, dim=0)
labels = torch.cat(labels, dim=0)
save_dict = {
'latents': latents,
'latents_flip': latents_flip,
'labels': labels
}
for key in save_dict:
if rank == 0:
print(key, save_dict[key].shape)
save_dict = {key: tensor.contiguous().cpu() for key, tensor in save_dict.items()}
save_filename = os.path.join(output_dir, f'latents_rank{rank:02d}_shard{saved_files:03d}.safetensors')
save_file(
save_dict,
save_filename,
metadata={'total_size': f'{latents.shape[0]}', 'dtype': f'{latents.dtype}', 'device': f'{latents.device}'}
)
if rank == 0:
print(f'Saved {save_filename}')
latents = []
latents_flip = []
labels = []
saved_files += 1
# save remainder latents that are fewer than 10000 images
if len(latents) > 0:
latents = torch.cat(latents, dim=0)
latents_flip = torch.cat(latents_flip, dim=0)
labels = torch.cat(labels, dim=0)
save_dict = {
'latents': latents,
'latents_flip': latents_flip,
'labels': labels
}
for key in save_dict:
if rank == 0:
print(key, save_dict[key].shape)
save_dict = {key: tensor.contiguous().cpu() for key, tensor in save_dict.items()}
save_filename = os.path.join(output_dir, f'latents_rank{rank:02d}_shard{saved_files:03d}.safetensors')
save_file(
save_dict,
save_filename,
metadata={'total_size': f'{latents.shape[0]}', 'dtype': f'{latents.dtype}', 'device': f'{latents.device}'}
)
if rank == 0:
print(f'Saved {save_filename}')
# Calculate latents stats
dist.barrier()
if rank == 0:
dataset = ImgLatentDataset(output_dir, latent_norm=True, sample=train_config['data']['sample'] if 'sample' in train_config['data'] else False,)
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# parser.add_argument("--data_path", type=str, default='/path/to/your/data')
parser.add_argument("--data_split", type=str, default='train')
parser.add_argument("--output_path", type=str, default="/data/dataset/imagenet/")
parser.add_argument("--image_size", type=int, default=256)
parser.add_argument("--batch_size", type=int, default=64)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument('--config', type=str, default='configs/debug.yaml')
args = parser.parse_args()
train_config = load_config(args.config)
main(args, train_config)
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