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# Modified from:
# fast-DiT: https://github.com/chuanyangjin/fast-DiT/blob/main/extract_features.py
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
import torch
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 import transforms
import numpy as np
import argparse
import os
import sys
current_directory = os.getcwd()
sys.path.append(current_directory)
from utils.distributed import init_distributed_mode
from dataset.augmentation import center_crop_arr
from dataset.build import build_dataset
from tokenizer.tokenizer_image.vq_model import VQ_models
from condition.hed import HEDdetector, ControlNetHED_Apache2
import cv2
from torch.nn.parallel import DataParallel
from einops import rearrange
from datasets import load_dataset
from torchvision import transforms
from PIL import Image
from language.t5 import T5Embedder
#################################################################################
# Training Loop #
#################################################################################
resolution = (512, 512)
image_transforms = transforms.Compose(
[
transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
conditioning_image_transforms = transforms.Compose(
[
transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.ToTensor(),
]
)
label_image_transforms = transforms.Compose(
[
transforms.Resize(resolution, interpolation=transforms.InterpolationMode.NEAREST, antialias=True),
]
)
def collate_fn(examples):
pil_images = [example['image'].convert("RGB") for example in examples]
images = [image_transforms(image) for image in pil_images]
images = torch.stack(images)
conditioning_images = [example['control_seg'].convert("RGB") for example in examples]
conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
conditioning_images = torch.stack(conditioning_images)
captions = [example['prompt'] for example in examples]
dtype = torch.long
# labels = [torch.from_numpy(np.array(example['panoptic_seg_map'])).unsqueeze(0) for example in examples] # seg_map panoptic_seg_map
# labels = [label_image_transforms(label) for label in labels]
# labels = torch.stack(labels)
labels = [example['seg_map'] for example in examples]
return {
"images": images, # -1~1
"conditioning_images": conditioning_images, # 0~1
"captions": captions,
"labels": labels
}
def main(args):
assert torch.cuda.is_available(), "Training currently requires at least one GPU."
# Setup DDP:
if not args.debug:
init_distributed_mode(args)
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
else:
device = 'cuda'
rank = 0
# Setup a feature folder:
if args.debug or rank == 0:
os.makedirs(args.code_path, exist_ok=True)
os.makedirs(os.path.join(args.code_path, f'code'), exist_ok=True)
os.makedirs(os.path.join(args.code_path, f'image'), exist_ok=True)
os.makedirs(os.path.join(args.code_path, f'control'), exist_ok=True)
os.makedirs(os.path.join(args.code_path, f'caption_emb'), exist_ok=True)
if args.split == 'validation':
os.makedirs(os.path.join(args.code_path, f'label'), exist_ok=True)
# create and load model
vq_model = VQ_models[args.vq_model](
codebook_size=args.codebook_size,
codebook_embed_dim=args.codebook_embed_dim)
vq_model.to(device)
vq_model.eval()
checkpoint = torch.load(args.vq_ckpt, map_location="cpu")
vq_model.load_state_dict(checkpoint["model"])
del checkpoint
t5_model = T5Embedder(
device=device,
local_cache=True,
cache_dir=args.t5_path,
dir_or_name=args.t5_model_type,
model_max_length=args.t5_feature_max_len,
)
# Setup data:
if args.ten_crop:
crop_size = int(args.image_size * args.crop_range)
transform = transforms.Compose([
transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, crop_size)),
transforms.TenCrop(args.image_size), # this is a tuple of PIL Images
transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), # returns a 4D tensor
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
else:
crop_size = args.image_size
transform = transforms.Compose([
transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, crop_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
# dataset = build_dataset(args, transform=transform)
# dataset = load_dataset(
# args.data_path,
# cache_dir=None,
# )
dataset = load_dataset(args.data_path, split=args.split, # "train" or "validation"
cache_dir=None,)
print(dataset)
if not args.debug:
sampler = DistributedSampler(
# dataset[args.split],
dataset,
num_replicas=dist.get_world_size(),
rank=rank,
shuffle=False,
seed=args.global_seed
)
else:
sampler = None
loader = DataLoader(
# dataset[args.split],
dataset,
batch_size=1, # important!
shuffle=False,
sampler=sampler,
num_workers=args.num_workers,
collate_fn=collate_fn,
pin_memory=True,
drop_last=False
)
from tqdm import tqdm
total = 0
code_len = 1024
t5_feature_max_len = 120
t5_feature_dim = 2048
max_seq_length = t5_feature_max_len + code_len
for batch in tqdm(loader):
captions = batch['captions']
train_steps = rank + total
img_save_path = f'{args.code_path}/image/{train_steps}.png'
cond_save_path = f'{args.code_path}/control/{train_steps}.png'
label_save_path = f'{args.code_path}/label/{train_steps}.png'
Image.fromarray((255*(batch['images'][0].numpy().transpose(1,2,0)*0.5+0.5)).astype('uint8'), mode='RGB').save(img_save_path)
Image.fromarray((255*batch['conditioning_images'][0].numpy().transpose(1,2,0)).astype('uint8'), mode='RGB').save(cond_save_path)
label = Image.fromarray(np.array(batch['labels'][0]).astype('uint8'))
label.resize((512,512), Image.Resampling.NEAREST).save(label_save_path)
with torch.no_grad():
_, _, [_, _, indices] = vq_model.encode(batch['images'].to(device))
caption_emb, emb_mask = t5_model.get_text_embeddings(captions)
valid_num = int(emb_mask.sum().item())
caption_emb = caption_emb[:, :valid_num]
codes = indices.reshape(1, 1, -1)
x = codes.detach().cpu().numpy() # (1, num_aug, args.image_size//16 * args.image_size//16)
np.save(f'{args.code_path}/code/{train_steps}.npy', x)
caption_emb = caption_emb.to(torch.float32).detach().cpu().numpy()
caption_dict = {}
caption_dict['prompt'] = captions
caption_dict['caption_emb'] = caption_emb
np.savez(f'{args.code_path}/caption_emb/{train_steps}.npz', **caption_dict)
# 👇 新增：保存 prompt 为 .txt 文件，方便外部评估
with open(f'{args.code_path}/prompt/{train_steps}.txt', 'w', encoding='utf-8') as f:
f.write(captions[0]) # 假设 batch_size = 1
if not args.debug:
total += dist.get_world_size()
else:
total += 1
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--code-path", type=str, required=True)
parser.add_argument("--data-path", type=str, required=True)
parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
parser.add_argument("--vq-ckpt", type=str, required=True, help="ckpt path for vq model")
parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
parser.add_argument("--dataset", type=str, default='imagenet')
parser.add_argument("--image-size", type=int, choices=[256, 384, 448, 512], default=256)
parser.add_argument("--ten-crop", action='store_true', help="whether using random crop")
parser.add_argument("--crop-range", type=float, default=1.1, help="expanding range of center crop")
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--num-workers", type=int, default=24)
parser.add_argument("--debug", action='store_true')
parser.add_argument("--min-threshold", type=int, default=200)
parser.add_argument("--max-threshold", type=int, default=400)
parser.add_argument("--t5-path", type=str, default='checkpoints/t5-ckpt')
parser.add_argument("--t5-model-type", type=str, default='flan-t5-xl')
parser.add_argument("--t5-feature-max-len", type=int, default=120)
parser.add_argument("--split", type=str, default='train',help="train or validation")
args = parser.parse_args()
main(args)
# # Modified from:
# # fast-DiT: https://github.com/chuanyangjin/fast-DiT/blob/main/extract_features.py
# import os
# # os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
# import torch
# 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 import transforms
# import numpy as np
# import argparse
# import os
# import sys
# current_directory = os.getcwd()
# sys.path.append(current_directory)
# from utils.distributed import init_distributed_mode
# from dataset.augmentation import center_crop_arr
# from dataset.build import build_dataset
# from tokenizer.tokenizer_image.vq_model import VQ_models
# # from condition.hed import HEDdetector, ControlNetHED_Apache2
# import cv2
# from torch.nn.parallel import DataParallel
# from einops import rearrange
# from datasets import load_dataset
# from torchvision import transforms
# from PIL import Image
# from language.t5 import T5Embedder
# #################################################################################
# # Training Loop #
# #################################################################################
# resolution = (512, 512)
# image_transforms = transforms.Compose(
# [
# transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BICUBIC, antialias=True),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5]),
# ]
# )
# conditioning_image_transforms = transforms.Compose(
# [
# transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BICUBIC, antialias=True),
# transforms.ToTensor(),
# ]
# )
# label_image_transforms = transforms.Compose(
# [
# transforms.Resize(resolution, interpolation=transforms.InterpolationMode.NEAREST, antialias=True),
# ]
# )
# def collate_fn(examples):
# print("Examples:", examples) # 打印原始数据结构
# print("Available keys:", examples[0].keys()) # 打印数据集中包含的键
# exit() # 直接终止，查看输出
# def collate_fn(examples):
# pil_images = [example['image'].convert("RGB") for example in examples]
# images = [image_transforms(image) for image in pil_images]
# images = torch.stack(images)
# conditioning_images = [example['control_seg'].convert("RGB") for example in examples]
# conditioning_images = [example["control_seg"][0].convert("RGB") for example in examples]
# conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
# conditioning_images = torch.stack(conditioning_images)
# # captions = [example['prompt'] for example in examples]
# # dtype = torch.long
# # # labels = [torch.from_numpy(np.array(example['panoptic_seg_map'])).unsqueeze(0) for example in examples] # seg_map panoptic_seg_map
# # # labels = [label_image_transforms(label) for label in labels]
# # # labels = torch.stack(labels)
# # labels = [example['seg_map'] for example in examples]
# # **修改 captions 逻辑**
# captions = [" ".join(example["scene"]) if "scene" in example else "unknown" for example in examples]
# labels = [example["control_seg"][0] for example in examples]
# return {
# "images": images, # -1~1
# "conditioning_images": conditioning_images, # 0~1
# "captions": captions,
# "labels": labels
# }
# def main(args):
# assert torch.cuda.is_available(), "Training currently requires at least one GPU."
# # Setup DDP:
# # if not args.debug:
# # init_distributed_mode(args)
# # rank = dist.get_rank()
# # device = rank % torch.cuda.device_count()
# # seed = args.global_seed * dist.get_world_size() + rank
# # torch.manual_seed(seed)
# # torch.cuda.set_device(device)
# # print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
# # else:
# # device = 'cuda'
# # rank = 0
# device = torch.device("cuda")
# # Setup a feature folder:
# # if args.debug or rank == 0:
# # os.makedirs(args.code_path, exist_ok=True)
# # os.makedirs(os.path.join(args.code_path, f'code'), exist_ok=True)
# # os.makedirs(os.path.join(args.code_path, f'image'), exist_ok=True)
# # os.makedirs(os.path.join(args.code_path, f'control'), exist_ok=True)
# # os.makedirs(os.path.join(args.code_path, f'caption_emb'), exist_ok=True)
# # if args.split == 'validation':
# # os.makedirs(os.path.join(args.code_path, f'label'), exist_ok=True)
# os.makedirs(args.code_path, exist_ok=True)
# os.makedirs(os.path.join(args.code_path, f'code'), exist_ok=True)
# os.makedirs(os.path.join(args.code_path, f'image'), exist_ok=True)
# os.makedirs(os.path.join(args.code_path, f'control'), exist_ok=True)
# os.makedirs(os.path.join(args.code_path, f'caption_emb'), exist_ok=True)
# if args.split == 'validation':
# os.makedirs(os.path.join(args.code_path, f'label'), exist_ok=True)
# # create and load model加载 VQ 模型
# vq_model = VQ_models[args.vq_model](
# codebook_size=args.codebook_size,
# codebook_embed_dim=args.codebook_embed_dim)
# vq_model.to(device)
# vq_model.eval()
# # 加载模型权重
# checkpoint = torch.load(args.vq_ckpt, map_location="cpu")
# vq_model.load_state_dict(checkpoint["model"])
# del checkpoint
# # 加载 T5 文本嵌入模型
# t5_model = T5Embedder(
# device=device,
# local_cache=True,
# cache_dir=args.t5_path,
# dir_or_name=args.t5_model_type,
# model_max_length=args.t5_feature_max_len,
# )
# # 以下注释
# # Setup data:
# if args.ten_crop:
# crop_size = int(args.image_size * args.crop_range)
# transform = transforms.Compose([
# transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, crop_size)),
# transforms.TenCrop(args.image_size), # this is a tuple of PIL Images
# transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), # returns a 4D tensor
# transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
# ])
# else:
# crop_size = args.image_size
# transform = transforms.Compose([
# transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, crop_size)),
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
# ])
# # dataset = build_dataset(args, transform=transform)
# dataset = load_dataset(
# args.data_path,
# cache_dir=None,
# ) # 读取数据集
# if not args.debug:
# sampler = DistributedSampler(
# dataset[args.split],
# num_replicas=dist.get_world_size(),
# rank=rank,
# shuffle=False,
# seed=args.global_seed
# )
# else:
# sampler = None
# loader = DataLoader(
# dataset[args.split],
# batch_size=1, # important!
# shuffle=False,
# sampler=sampler,
# num_workers=args.num_workers,
# collate_fn=collate_fn,
# pin_memory=True,
# drop_last=False
# )
# # 以上注释并添加下边的数据预处理
# # # 设置数据预处理
# # crop_size = int(args.image_size * args.crop_range) if args.ten_crop else args.image_size
# # transform = transforms.Compose([
# # transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, crop_size)),
# # transforms.ToTensor(),
# # transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
# # ])
# # # 加载数据集
# # dataset = load_dataset(args.data_path, cache_dir=None)
# # # **修改 DataLoader**
# # loader = DataLoader(
# # dataset[args.split],
# # batch_size=1, # 重要！
# # shuffle=True, # **单 GPU 训练时使用 shuffle**
# # num_workers=args.num_workers,
# # collate_fn=collate_fn,
# # pin_memory=True,
# # drop_last=False
# # )
# from tqdm import tqdm
# total = 0
# code_len = 1024
# t5_feature_max_len = 120
# t5_feature_dim = 2048
# max_seq_length = t5_feature_max_len + code_len
# for batch in tqdm(loader):
# captions = batch['captions']
# # train_steps = rank + total
# img_save_path = f'{args.code_path}/image/{total}.png'
# cond_save_path = f'{args.code_path}/control/{total}.png'
# label_save_path = f'{args.code_path}/label/{total}.png'
# Image.fromarray((255*(batch['images'][0].numpy().transpose(1,2,0)*0.5+0.5)).astype('uint8'), mode='RGB').save(img_save_path)
# Image.fromarray((255*batch['conditioning_images'][0].numpy().transpose(1,2,0)).astype('uint8'), mode='RGB').save(cond_save_path)
# label = Image.fromarray(np.array(batch['labels'][0]).astype('uint8'))
# label.resize((512,512), Image.Resampling.NEAREST).save(label_save_path)
# with torch.no_grad():
# _, _, [_, _, indices] = vq_model.encode(batch['images'].to(device))
# caption_emb, emb_mask = t5_model.get_text_embeddings(captions)
# valid_num = int(emb_mask.sum().item())
# caption_emb = caption_emb[:, :valid_num]
# codes = indices.reshape(1, 1, -1)
# x = codes.detach().cpu().numpy() # (1, num_aug, args.image_size//16 * args.image_size//16)
# np.save(f'{args.code_path}/code/{total}.npy', x) # 保存将图像编码成的离散Token
# caption_emb = caption_emb.to(torch.float32).detach().cpu().numpy()
# caption_dict = {}
# caption_dict['prompt'] = captions
# caption_dict['caption_emb'] = caption_emb
# np.savez(f'{args.code_path}/caption_emb/{total}.npz', **caption_dict) # 保存文本的 T5 Embeddings
# # if not args.debug:
# # total += dist.get_world_size()
# # else:
# # total += 1
# total += 1
# dist.destroy_process_group()
# if __name__ == "__main__":
# parser = argparse.ArgumentParser()
# parser.add_argument("--code-path", type=str, required=True)
# parser.add_argument("--data-path", type=str, required=True)
# parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
# parser.add_argument("--vq-ckpt", type=str, required=True, help="ckpt path for vq model")
# parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
# parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
# parser.add_argument("--dataset", type=str, default='imagenet')
# parser.add_argument("--image-size", type=int, choices=[256, 384, 448, 512], default=256)
# parser.add_argument("--ten-crop", action='store_true', help="whether using random crop")
# parser.add_argument("--crop-range", type=float, default=1.1, help="expanding range of center crop")
# parser.add_argument("--global-seed", type=int, default=0)
# parser.add_argument("--num-workers", type=int, default=24)
# parser.add_argument("--debug", action='store_true')
# parser.add_argument("--min-threshold", type=int, default=200)
# parser.add_argument("--max-threshold", type=int, default=400)
# parser.add_argument("--t5-path", type=str, default='checkpoints/t5-ckpt')
# parser.add_argument("--t5-model-type", type=str, default='flan-t5-xl')
# parser.add_argument("--t5-feature-max-len", type=int, default=120)
# parser.add_argument("--split", type=str, default='train')
# args = parser.parse_args()
# main(args)
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