tex3 / src /laion_bytenas.py
hanshu.yan
add app.py
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import os
import json
import random
from tqdm import tqdm
import numpy as np
from PIL import Image, ImageStat
import torch
from torch.utils.data import Dataset, DataLoader, IterableDataset, get_worker_info
from torchvision import transforms as T
### >>>>>>>> >>>>>>>> text related >>>>>>>> >>>>>>>> ###
class TokenizerWrapper():
def __init__(self, tokenizer, is_train, proportion_empty_prompts, use_generic_prompts=False):
self.tokenizer = tokenizer
self.is_train = is_train
self.proportion_empty_prompts = proportion_empty_prompts
self.use_generic_prompts = use_generic_prompts
def __call__(self, prompts):
if isinstance(prompts, str):
prompts = [prompts]
captions = []
for caption in prompts:
if random.random() < self.proportion_empty_prompts:
captions.append("")
else:
if self.use_generic_prompts:
captions.append("best quality, high quality")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if self.is_train else caption[0])
else:
raise ValueError(
f"Caption column should contain either strings or lists of strings."
)
inputs = self.tokenizer(
captions, max_length=self.tokenizer.model_max_length, padding="max_length",
truncation=True, return_tensors="pt"
)
return inputs.input_ids
### >>>>>>>> >>>>>>>> image related >>>>>>>> >>>>>>>> ###
MONOCHROMATIC_MAX_VARIANCE = 0.3
def is_monochromatic_image(pil_img):
v = ImageStat.Stat(pil_img.convert('RGB')).var
return sum(v)<MONOCHROMATIC_MAX_VARIANCE
def isnumeric(text):
return (''.join(filter(str.isalnum, text))).isnumeric()
class TextPromptDataset(IterableDataset):
'''
The dataset for (text embedding, noise, generated latent) triplets.
'''
def __init__(self,
data_root,
tokenizer = None,
transform = None,
rank = 0,
world_size = 1,
shuffle = True,
):
self.tokenizer = tokenizer
self.transform = transform
self.img_root = os.path.join(data_root, 'JPEGImages')
self.data_list = []
print("#### Loading filename list...")
json_root = os.path.join(data_root, 'list')
json_list = [p for p in os.listdir(json_root) if p.startswith("shard") and p.endswith('.json')]
# duplicate several shards to make sure each process has the same number of shards
assert len(json_list) > world_size
duplicate = world_size - len(json_list)%world_size if len(json_list)%world_size>0 else 0
json_list = json_list + json_list[:duplicate]
json_list = json_list[rank::world_size]
for json_file in tqdm(json_list):
shard_name = os.path.basename(json_file).split('.')[0]
with open(os.path.join(json_root, json_file)) as f:
key_text_pairs = json.load(f)
for pair in key_text_pairs:
self.data_list.append( [shard_name] + pair )
print("#### All filename loaded...")
self.shuffle = shuffle
def __len__(self):
return len(self.data_list)
def __iter__(self):
worker_info = get_worker_info()
if worker_info is None: # single-process data loading, return the full iterator
data_list = self.data_list
else:
len_data = len(self.data_list) - len(self.data_list) % worker_info.num_workers
data_list = self.data_list[:len_data][worker_info.id :: worker_info.num_workers]
# print(worker_info.num_workers, worker_info.id, len(data_list)/len(self.data_list))
if self.shuffle:
random.shuffle(data_list)
while True:
for idx in range(len(data_list)):
# try:
shard_name = data_list[idx][0]
data = {}
img_file = data_list[idx][1]
img = Image.open(os.path.join(self.img_root, shard_name, img_file+'.jpg')).convert("RGB")
if is_monochromatic_image(img):
continue
if self.transform is not None:
img = self.transform(img)
data['pixel_values'] = img
text = data_list[idx][2]
if self.tokenizer is not None:
if isinstance(self.tokenizer, list):
assert len(self.tokenizer)==2
data['input_ids'] = self.tokenizer[0](text)[0]
data['input_ids_2'] = self.tokenizer[1](text)[0]
else:
data['input_ids'] = self.tokenizer(text)[0]
else:
data['input_ids'] = text
yield data
# except Exception as e:
# raise(e)
def collate_fn(self, examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
if self.tokenizer is not None:
if isinstance(self.tokenizer, list):
assert len(self.tokenizer)==2
input_ids = torch.stack([example["input_ids"] for example in examples])
input_ids_2 = torch.stack([example["input_ids_2"] for example in examples])
return {"pixel_values": pixel_values, "input_ids": input_ids, "input_ids_2": input_ids_2,}
else:
input_ids = torch.stack([example["input_ids"] for example in examples])
return {"pixel_values": pixel_values, "input_ids": input_ids,}
else:
input_ids = [example["input_ids"] for example in examples]
return {"pixel_values": pixel_values, "input_ids": input_ids,}
def make_train_dataset(
train_data_path,
size = 512,
tokenizer=None,
cfg_drop_ratio=0,
rank=0,
world_size=1,
shuffle=True,
):
_image_transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize(size),
T.CenterCrop((size,size)),
T.ToTensor(),
T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if tokenizer is not None:
if isinstance(tokenizer, list):
assert len(tokenizer)==2
tokenizer_1 = TokenizerWrapper(
tokenizer[0],
is_train=True,
proportion_empty_prompts=cfg_drop_ratio,
use_generic_prompts=False,
)
tokenizer_2 = TokenizerWrapper(
tokenizer[1],
is_train=True,
proportion_empty_prompts=cfg_drop_ratio,
use_generic_prompts=False,
)
tokenizer = [tokenizer_1, tokenizer_2]
else:
tokenizer = TokenizerWrapper(
tokenizer,
is_train=True,
proportion_empty_prompts=cfg_drop_ratio,
use_generic_prompts=False,
)
train_dataset = TextPromptDataset(
data_root=train_data_path,
transform=_image_transform,
rank=rank,
world_size=world_size,
tokenizer=tokenizer,
shuffle=shuffle,
)
return train_dataset
### >>>>>>>> >>>>>>>> Test >>>>>>>> >>>>>>>> ###
if __name__ == "__main__":
from transformers import CLIPTextModel, CLIPTokenizer
tokenizer = CLIPTokenizer.from_pretrained(
"/mnt/bn/ic-research-aigc-editing/fast-diffusion-models/assets/public_models/StableDiffusion/stable-diffusion-v1-5",
subfolder="tokenizer"
)
train_dataset = make_train_dataset(tokenizer=tokenizer, rank=0, world_size=10)
loader = torch.utils.data.DataLoader(
train_dataset, batch_size=64, num_workers=0,
collate_fn=train_dataset.collect_fn if hasattr(train_dataset, 'collect_fn') else None,
)
for batch in loader:
pixel_values = batch["pixel_values"]
prompt_ids = batch['input_ids']
from einops import rearrange
pixel_values = rearrange(pixel_values, 'b c h w -> b h w c')
for i in range(pixel_values.shape[0]):
import pdb; pdb.set_trace()
Image.fromarray(((pixel_values[i] + 1 )/2 * 255 ).numpy().astype(np.uint8)).save('tmp.png')
input_id = prompt_ids[i]
text = tokenizer.decode(input_id).split('<|startoftext|>')[-1].split('<|endoftext|>')[0]
print(text)
pass