wehub-resource-sync
493 lines
19 KiB
Python
Executable File
493 lines
19 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import getpass
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import json
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import os
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import os.path as osp
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import random
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import numpy as np
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import torch
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import torch.distributed as dist
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from PIL import Image
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from termcolor import colored
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from torch.utils.data import Dataset
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from diffusion.data.builder import DATASETS
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from diffusion.data.wids import ShardListDataset, ShardListDatasetMulti, lru_json_load
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from diffusion.utils.logger import get_root_logger
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@DATASETS.register_module()
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class SanaImgDataset(Dataset):
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def __init__(
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self,
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data_dir="",
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transform=None,
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resolution=256,
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load_vae_feat=False,
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load_text_feat=False,
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max_length=300,
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config=None,
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caption_proportion=None,
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external_caption_suffixes=None,
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external_clipscore_suffixes=None,
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clip_thr=0.0,
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clip_thr_temperature=1.0,
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img_extension=".png",
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**kwargs,
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):
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if external_caption_suffixes is None:
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external_caption_suffixes = []
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if external_clipscore_suffixes is None:
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external_clipscore_suffixes = []
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self.logger = (
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get_root_logger() if config is None else get_root_logger(osp.join(config.work_dir, "train_log.log"))
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)
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self.transform = transform if not load_vae_feat else None
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self.load_vae_feat = load_vae_feat
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self.load_text_feat = load_text_feat
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self.resolution = resolution
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self.max_length = max_length
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self.caption_proportion = caption_proportion if caption_proportion is not None else {"prompt": 1.0}
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self.external_caption_suffixes = external_caption_suffixes
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self.external_clipscore_suffixes = external_clipscore_suffixes
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self.clip_thr = clip_thr
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self.clip_thr_temperature = clip_thr_temperature
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self.default_prompt = "prompt"
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self.img_extension = img_extension
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self.data_dirs = data_dir if isinstance(data_dir, list) else [data_dir]
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# self.meta_datas = [osp.join(data_dir, "meta_data.json") for data_dir in self.data_dirs]
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self.dataset = []
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for data_dir in self.data_dirs:
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meta_data = json.load(open(osp.join(data_dir, "meta_data.json")))
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self.dataset.extend([osp.join(data_dir, i) for i in meta_data["img_names"]])
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self.dataset = self.dataset * 2000
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self.logger.info(colored("Dataset is repeat 2000 times for toy dataset", "red", attrs=["bold"]))
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self.ori_imgs_nums = len(self)
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self.logger.info(f"Dataset samples: {len(self.dataset)}")
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self.logger.info(f"Loading external caption json from: original_filename{external_caption_suffixes}.json")
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self.logger.info(f"Loading external clipscore json from: original_filename{external_clipscore_suffixes}.json")
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self.logger.info(f"external caption clipscore threshold: {clip_thr}, temperature: {clip_thr_temperature}")
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self.logger.info(f"Text max token length: {self.max_length}")
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def getdata(self, idx):
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data = self.dataset[idx]
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img_extensions = [".jpg", ".png", ".jpeg", ".webp"]
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filename, ext = os.path.splitext(data)
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if ext in img_extensions:
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data = filename
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self.img_extension = ext
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self.key = data.split("/")[-1]
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info = {}
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with open(f"{data}.txt") as f:
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info[self.default_prompt] = f.readlines()[0].strip()
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# external json file
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for suffix in self.external_caption_suffixes:
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caption_json_path = f"{data}{suffix}.json"
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if os.path.exists(caption_json_path):
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try:
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caption_json = lru_json_load(caption_json_path)
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except:
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caption_json = {}
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if self.key in caption_json:
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info.update(caption_json[self.key])
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caption_type, caption_clipscore = self.weighted_sample_clipscore(data, info)
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caption_type = caption_type if caption_type in info else self.default_prompt
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txt_fea = "" if info[caption_type] is None else info[caption_type]
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data_info = {
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"img_hw": torch.tensor([self.resolution, self.resolution], dtype=torch.float32),
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"aspect_ratio": torch.tensor(1.0),
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}
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if self.load_vae_feat:
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assert ValueError("Load VAE is not supported now")
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else:
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img = f"{data}{self.img_extension}"
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img = Image.open(img)
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if self.transform:
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img = self.transform(img)
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attention_mask = torch.ones(1, 1, self.max_length, dtype=torch.int16) # 1x1xT
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if self.load_text_feat:
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npz_path = f"{self.key}.npz"
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txt_info = np.load(npz_path)
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txt_fea = torch.from_numpy(txt_info["caption_feature"]) # 1xTx4096
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if "attention_mask" in txt_info:
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attention_mask = torch.from_numpy(txt_info["attention_mask"])[None]
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# make sure the feature length are the same
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if txt_fea.shape[1] != self.max_length:
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txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_length - txt_fea.shape[1], 1)], dim=1)
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attention_mask = torch.cat(
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[attention_mask, torch.zeros(1, 1, self.max_length - attention_mask.shape[-1])], dim=-1
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)
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return (
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img,
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txt_fea,
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attention_mask.to(torch.int16),
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data_info,
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idx,
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caption_type,
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"",
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str(caption_clipscore),
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)
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def __getitem__(self, idx):
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for _ in range(10):
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try:
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data = self.getdata(idx)
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return data
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except Exception as e:
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print(f"Error details: {str(e)}")
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idx = (idx + 1) % len(self)
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raise RuntimeError("Too many bad data.")
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def __len__(self):
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return len(self.dataset)
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def weighted_sample_fix_prob(self):
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labels = list(self.caption_proportion.keys())
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weights = list(self.caption_proportion.values())
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sampled_label = random.choices(labels, weights=weights, k=1)[0]
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return sampled_label
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def weighted_sample_clipscore(self, data, info):
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labels = []
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weights = []
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fallback_label = None
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max_clip_score = float("-inf")
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for suffix in self.external_clipscore_suffixes:
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clipscore_json_path = f"{data}{suffix}.json"
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if os.path.exists(clipscore_json_path):
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try:
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clipscore_json = lru_json_load(clipscore_json_path)
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except:
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clipscore_json = {}
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if self.key in clipscore_json:
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clip_scores = clipscore_json[self.key]
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for caption_type, clip_score in clip_scores.items():
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clip_score = float(clip_score)
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if caption_type in info:
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if clip_score >= self.clip_thr:
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labels.append(caption_type)
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weights.append(clip_score)
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if clip_score > max_clip_score:
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max_clip_score = clip_score
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fallback_label = caption_type
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if not labels and fallback_label:
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return fallback_label, max_clip_score
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if not labels:
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return self.default_prompt, 0.0
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adjusted_weights = np.array(weights) ** (1.0 / max(self.clip_thr_temperature, 0.01))
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normalized_weights = adjusted_weights / np.sum(adjusted_weights)
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sampled_label = random.choices(labels, weights=normalized_weights, k=1)[0]
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# sampled_label = random.choices(labels, weights=[1]*len(weights), k=1)[0]
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index = labels.index(sampled_label)
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original_weight = weights[index]
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return sampled_label, original_weight
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@DATASETS.register_module()
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class SanaWebDataset(Dataset):
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def __init__(
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self,
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data_dir="",
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meta_path=None,
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cache_dir="/cache/data/sana-webds-meta",
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max_shards_to_load=None,
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transform=None,
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resolution=256,
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load_vae_feat=False,
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load_text_feat=False,
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max_length=300,
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config=None,
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caption_proportion=None,
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caption_selection_type="clipscore", # clipscore, proportion
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sort_dataset=False,
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num_replicas=None,
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external_caption_suffixes=None,
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external_clipscore_suffixes=None,
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clip_thr=0.0,
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clip_thr_temperature=1.0,
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**kwargs,
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):
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if external_caption_suffixes is None:
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external_caption_suffixes = []
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if external_clipscore_suffixes is None:
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external_clipscore_suffixes = []
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self.logger = (
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get_root_logger() if config is None else get_root_logger(osp.join(config.work_dir, "train_log.log"))
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)
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self.transform = transform if not load_vae_feat else None
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self.load_vae_feat = load_vae_feat
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self.load_text_feat = load_text_feat
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self.resolution = resolution
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self.max_length = max_length
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self.caption_selection_type = caption_selection_type
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self.caption_proportion = caption_proportion if caption_proportion is not None else {"prompt": 1.0}
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self.external_caption_suffixes = external_caption_suffixes
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self.external_clipscore_suffixes = external_clipscore_suffixes
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self.clip_thr = clip_thr
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self.clip_thr_temperature = clip_thr_temperature
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self.default_prompt = "prompt"
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data_dirs = data_dir if isinstance(data_dir, list) else [data_dir]
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meta_paths = meta_path if isinstance(meta_path, list) else [meta_path] * len(data_dirs)
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self.meta_paths = []
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for data_path, meta_path in zip(data_dirs, meta_paths):
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self.data_path = osp.expanduser(data_path)
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self.meta_path = osp.expanduser(meta_path) if meta_path is not None else None
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_local_meta_path = osp.join(self.data_path, "wids-meta.json")
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if meta_path is None and osp.exists(_local_meta_path):
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self.logger.info(f"loading from {_local_meta_path}")
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self.meta_path = meta_path = _local_meta_path
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if meta_path is None:
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self.meta_path = osp.join(
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osp.expanduser(cache_dir),
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self.data_path.replace("/", "--") + f".max_shards:{max_shards_to_load}" + ".wdsmeta.json",
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)
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assert osp.exists(self.meta_path), f"meta path not found in [{self.meta_path}] or [{_local_meta_path}]"
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self.logger.info(f"[SimplyInternal] Loading meta information {self.meta_path}")
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self.meta_paths.append(self.meta_path)
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self._initialize_dataset(num_replicas, sort_dataset)
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if len(self.external_caption_suffixes) > 0:
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self.logger.info(f"Loading external caption json from: original_filename{external_caption_suffixes}.json")
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if len(self.external_clipscore_suffixes) > 0:
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self.logger.info(
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f"Loading external clipscore json from: original_filename{external_clipscore_suffixes}.json"
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)
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self.logger.info(f"external caption clipscore threshold: {clip_thr}, temperature: {clip_thr_temperature}")
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self.logger.info(f"Text max token length: {self.max_length}")
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self.logger.warning(f"Sort the dataset: {sort_dataset}")
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def _initialize_dataset(self, num_replicas, sort_dataset):
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# uuid = abs(hash(self.meta_path)) % (10 ** 8)
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import hashlib
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uuid = hashlib.sha256(self.meta_path.encode()).hexdigest()[:8]
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if len(self.meta_paths) > 0:
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self.dataset = ShardListDatasetMulti(
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self.meta_paths,
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cache_dir=osp.expanduser(f"~/.cache/_wids_cache/{getpass.getuser()}-{uuid}"),
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sort_data_inseq=sort_dataset,
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num_replicas=num_replicas or dist.get_world_size(),
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)
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else:
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# TODO: tmp to ensure there is no bug
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self.dataset = ShardListDataset(
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self.meta_path,
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cache_dir=osp.expanduser(f"~/.cache/_wids_cache/{getpass.getuser()}-{uuid}"),
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)
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self.ori_imgs_nums = len(self)
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self.logger.info(f"{self.dataset.data_info}")
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def getdata(self, idx):
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data = self.dataset[idx]
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info = data[".json"]
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self.key = data["__key__"]
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dataindex_info = {
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"index": data["__index__"],
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"shard": "/".join(data["__shard__"].rsplit("/", 2)[-2:]),
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"shardindex": data["__shardindex__"],
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}
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# external json file
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for suffix in self.external_caption_suffixes:
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caption_json_path = data["__shard__"].replace(".tar", f"{suffix}.json")
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if os.path.exists(caption_json_path):
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try:
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caption_json = lru_json_load(caption_json_path)
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except:
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caption_json = {}
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if self.key in caption_json:
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info.update(caption_json[self.key])
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caption_type, caption_clipscore = self.weighted_sample_clipscore(data, info)
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caption_type = caption_type if caption_type in info else self.default_prompt
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txt_fea = "" if info[caption_type] is None else info[caption_type]
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data_info = {
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"img_hw": torch.tensor([self.resolution, self.resolution], dtype=torch.float32),
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"aspect_ratio": torch.tensor(1.0),
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}
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if self.load_vae_feat:
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img = data[".npy"]
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else:
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img = data[".png"] if ".png" in data else data[".jpg"]
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if self.transform:
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img = self.transform(img)
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attention_mask = torch.ones(1, 1, self.max_length, dtype=torch.int16) # 1x1xT
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if self.load_text_feat:
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npz_path = f"{self.key}.npz"
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txt_info = np.load(npz_path)
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txt_fea = torch.from_numpy(txt_info["caption_feature"]) # 1xTx4096
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if "attention_mask" in txt_info:
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attention_mask = torch.from_numpy(txt_info["attention_mask"])[None]
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# make sure the feature length are the same
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if txt_fea.shape[1] != self.max_length:
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txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_length - txt_fea.shape[1], 1)], dim=1)
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attention_mask = torch.cat(
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[attention_mask, torch.zeros(1, 1, self.max_length - attention_mask.shape[-1])], dim=-1
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)
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return (
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img,
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txt_fea,
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attention_mask.to(torch.int16),
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data_info,
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idx,
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caption_type,
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dataindex_info,
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str(caption_clipscore),
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)
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def __getitem__(self, idx):
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for _ in range(10):
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try:
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data = self.getdata(idx)
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return data
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except Exception as e:
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print(f"Error details: {str(e)}")
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idx = idx + 1
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raise RuntimeError("Too many bad data.")
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def __len__(self):
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return len(self.dataset)
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def weighted_sample_fix_prob(self):
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labels = list(self.caption_proportion.keys())
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weights = list(self.caption_proportion.values())
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sampled_label = random.choices(labels, weights=weights, k=1)[0]
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return sampled_label
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def weighted_sample_clipscore(self, data, info):
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labels = []
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weights = []
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fallback_label = None
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max_clip_score = float("-inf")
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for suffix in self.external_clipscore_suffixes:
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clipscore_json_path = data["__shard__"].replace(".tar", f"{suffix}.json")
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if os.path.exists(clipscore_json_path):
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try:
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clipscore_json = lru_json_load(clipscore_json_path)
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except:
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clipscore_json = {}
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if self.key in clipscore_json:
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clip_scores = clipscore_json[self.key]
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for caption_type, clip_score in clip_scores.items():
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clip_score = float(clip_score)
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if caption_type in info:
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if clip_score >= self.clip_thr:
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labels.append(caption_type)
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weights.append(clip_score)
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if clip_score > max_clip_score:
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max_clip_score = clip_score
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fallback_label = caption_type
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if not labels and fallback_label:
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return fallback_label, max_clip_score
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if not labels:
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return self.default_prompt, 0.0
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adjusted_weights = np.array(weights) ** (1.0 / max(self.clip_thr_temperature, 0.01))
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normalized_weights = adjusted_weights / np.sum(adjusted_weights)
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sampled_label = random.choices(labels, weights=normalized_weights, k=1)[0]
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# sampled_label = random.choices(labels, weights=[1]*len(weights), k=1)[0]
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index = labels.index(sampled_label)
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original_weight = weights[index]
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return sampled_label, original_weight
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def get_data_info(self, idx):
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try:
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data = self.dataset[idx]
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info = data[".json"]
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key = data["__key__"]
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version = info.get("version", "others")
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default_promtp_clipscore_json = data["__shard__"].replace(".tar", f"_{self.default_prompt}_clip_score.json")
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if osp.exists(default_promtp_clipscore_json):
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clipscore_json = lru_json_load(default_promtp_clipscore_json)
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clip_score = float(clipscore_json[key][self.default_prompt])
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else:
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clip_score = 100
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return {
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"height": info["height"],
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"width": info["width"],
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"version": version,
|
|
"key": key,
|
|
"clipscore": clip_score,
|
|
"index": data["__index__"],
|
|
"__shard__": data["__shard__"],
|
|
"shardindex": data["__shardindex__"],
|
|
}
|
|
except Exception as e:
|
|
print(f"Error details: {str(e)}")
|
|
return None
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from torch.utils.data import DataLoader
|
|
|
|
from diffusion.data.transforms import get_transform
|
|
|
|
image_size = 1024 # 256
|
|
transform = get_transform("default_train", image_size)
|
|
train_dataset = SanaWebDataset(
|
|
data_dir="debug_data_train/vaef32c32/debug_data",
|
|
resolution=image_size,
|
|
transform=transform,
|
|
max_length=300,
|
|
load_vae_feat=True,
|
|
num_replicas=1,
|
|
)
|
|
dataloader = DataLoader(train_dataset, batch_size=32, shuffle=False, num_workers=4)
|
|
|
|
for data in dataloader:
|
|
img, txt_fea, attention_mask, data_info = data
|
|
print(txt_fea)
|
|
break
|