Spaces:
Runtime error
Runtime error
File size: 10,723 Bytes
f1f9265 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import os
import random
import numpy as np
import torch
from torchvision import transforms as T
from torchvision.transforms.functional import InterpolationMode
from tqdm import tqdm
from diffusion.data.builder import DATASETS
from diffusion.data.datasets.sana_data import SanaWebDataset
from diffusion.data.datasets.utils import *
from diffusion.data.wids import lru_json_load
def get_closest_ratio(height: float, width: float, ratios: dict):
aspect_ratio = height / width
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
return ratios[closest_ratio], float(closest_ratio)
@DATASETS.register_module()
class SanaWebDatasetMS(SanaWebDataset):
def __init__(
self,
data_dir="",
meta_path=None,
cache_dir="/cache/data/sana-webds-meta",
max_shards_to_load=None,
transform=None,
resolution=256,
sample_subset=None,
load_vae_feat=False,
load_text_feat=False,
input_size=32,
patch_size=2,
max_length=300,
config=None,
caption_proportion=None,
sort_dataset=False,
num_replicas=None,
external_caption_suffixes=None,
external_clipscore_suffixes=None,
clip_thr=0.0,
clip_thr_temperature=1.0,
vae_downsample_rate=32,
**kwargs,
):
super().__init__(
data_dir=data_dir,
meta_path=meta_path,
cache_dir=cache_dir,
max_shards_to_load=max_shards_to_load,
transform=transform,
resolution=resolution,
sample_subset=sample_subset,
load_vae_feat=load_vae_feat,
load_text_feat=load_text_feat,
input_size=input_size,
patch_size=patch_size,
max_length=max_length,
config=config,
caption_proportion=caption_proportion,
sort_dataset=sort_dataset,
num_replicas=num_replicas,
external_caption_suffixes=external_caption_suffixes,
external_clipscore_suffixes=external_clipscore_suffixes,
clip_thr=clip_thr,
clip_thr_temperature=clip_thr_temperature,
vae_downsample_rate=32,
**kwargs,
)
self.base_size = int(kwargs["aspect_ratio_type"].split("_")[-1])
self.aspect_ratio = eval(kwargs.pop("aspect_ratio_type")) # base aspect ratio
self.ratio_index = {}
self.ratio_nums = {}
self.interpolate_model = InterpolationMode.BICUBIC
self.interpolate_model = (
InterpolationMode.BICUBIC
if self.aspect_ratio not in [ASPECT_RATIO_2048, ASPECT_RATIO_2880]
else InterpolationMode.LANCZOS
)
for k, v in self.aspect_ratio.items():
self.ratio_index[float(k)] = []
self.ratio_nums[float(k)] = 0
self.vae_downsample_rate = vae_downsample_rate
def __getitem__(self, idx):
for _ in range(10):
try:
data = self.getdata(idx)
return data
except Exception as e:
print(f"Error details: {str(e)}")
idx = random.choice(self.ratio_index[self.closest_ratio])
raise RuntimeError("Too many bad data.")
def getdata(self, idx):
data = self.dataset[idx]
info = data[".json"]
self.key = data["__key__"]
dataindex_info = {
"index": data["__index__"],
"shard": "/".join(data["__shard__"].rsplit("/", 2)[-2:]),
"shardindex": data["__shardindex__"],
}
# external json file
for suffix in self.external_caption_suffixes:
caption_json_path = data["__shard__"].replace(".tar", f"{suffix}.json")
if os.path.exists(caption_json_path):
try:
caption_json = lru_json_load(caption_json_path)
except:
caption_json = {}
if self.key in caption_json:
info.update(caption_json[self.key])
data_info = {}
ori_h, ori_w = info["height"], info["width"]
# Calculate the closest aspect ratio and resize & crop image[w, h]
closest_size, closest_ratio = get_closest_ratio(ori_h, ori_w, self.aspect_ratio)
closest_size = list(map(lambda x: int(x), closest_size))
self.closest_ratio = closest_ratio
data_info["img_hw"] = torch.tensor([ori_h, ori_w], dtype=torch.float32)
data_info["aspect_ratio"] = closest_ratio
caption_type, caption_clipscore = self.weighted_sample_clipscore(data, info)
caption_type = caption_type if caption_type in info else self.default_prompt
txt_fea = "" if info[caption_type] is None else info[caption_type]
if self.load_vae_feat:
img = data[".npy"]
if len(img.shape) == 4 and img.shape[0] == 1:
img = img[0]
h, w = (img.shape[1], img.shape[2])
assert h == int(closest_size[0] // self.vae_downsample_rate) and w == int(
closest_size[1] // self.vae_downsample_rate
), f"h: {h}, w: {w}, ori_hw: {closest_size}, data_info: {dataindex_info}"
else:
img = data[".png"] if ".png" in data else data[".jpg"]
if closest_size[0] / ori_h > closest_size[1] / ori_w:
resize_size = closest_size[0], int(ori_w * closest_size[0] / ori_h)
else:
resize_size = int(ori_h * closest_size[1] / ori_w), closest_size[1]
self.transform = T.Compose(
[
T.Lambda(lambda img: img.convert("RGB")),
T.Resize(resize_size, interpolation=self.interpolate_model), # Image.BICUBIC
T.CenterCrop(closest_size),
T.ToTensor(),
T.Normalize([0.5], [0.5]),
]
)
if idx not in self.ratio_index[closest_ratio]:
self.ratio_index[closest_ratio].append(idx)
if self.transform:
img = self.transform(img)
attention_mask = torch.ones(1, 1, self.max_length, dtype=torch.int16) # 1x1xT
if self.load_text_feat:
npz_path = f"{self.key}.npz"
txt_info = np.load(npz_path)
txt_fea = torch.from_numpy(txt_info["caption_feature"]) # 1xTx4096
if "attention_mask" in txt_info:
attention_mask = torch.from_numpy(txt_info["attention_mask"])[None]
# make sure the feature length are the same
if txt_fea.shape[1] != self.max_length:
txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_length - txt_fea.shape[1], 1)], dim=1)
attention_mask = torch.cat(
[attention_mask, torch.zeros(1, 1, self.max_length - attention_mask.shape[-1])], dim=-1
)
return (
img,
txt_fea,
attention_mask.to(torch.int16),
data_info,
idx,
caption_type,
dataindex_info,
str(caption_clipscore),
)
def __len__(self):
return len(self.dataset)
@DATASETS.register_module()
class DummyDatasetMS(SanaWebDatasetMS):
def __init__(self, **kwargs):
self.base_size = int(kwargs["aspect_ratio_type"].split("_")[-1])
self.aspect_ratio = eval(kwargs.pop("aspect_ratio_type")) # base aspect ratio
self.ratio_index = {}
self.ratio_nums = {}
self.interpolate_model = InterpolationMode.BICUBIC
self.interpolate_model = (
InterpolationMode.BICUBIC
if self.aspect_ratio not in [ASPECT_RATIO_2048, ASPECT_RATIO_2880]
else InterpolationMode.LANCZOS
)
for k, v in self.aspect_ratio.items():
self.ratio_index[float(k)] = []
self.ratio_nums[float(k)] = 0
self.ori_imgs_nums = 1_000_000
self.height = 384
self.width = 672
def __getitem__(self, idx):
img = torch.randn((3, self.height, self.width))
txt_fea = "The image depicts a young woman standing in the middle of a street, leaning against a silver car. She is dressed in a stylish outfit consisting of a blue blouse and black pants. Her hair is long and dark, and she is looking directly at the camera with a confident expression. The street is lined with colorful buildings, and the trees have autumn leaves, suggesting the season is fall. The lighting is warm, with sunlight casting long shadows on the street. There are a few people in the background, and the overall atmosphere is vibrant and lively."
attention_mask = torch.ones(1, 1, 300, dtype=torch.int16) # 1x1xT
data_info = {"img_hw": torch.tensor([816.0, 1456.0]), "aspect_ratio": 0.57}
idx = 2500
caption_type = self.default_prompt
dataindex_info = {"index": 2500, "shard": "data_for_test_after_change/00000000.tar", "shardindex": 2500}
return img, txt_fea, attention_mask, data_info, idx, caption_type, dataindex_info
def __len__(self):
return self.ori_imgs_nums
def get_data_info(self, idx):
return {"height": self.height, "width": self.width, "version": "1.0", "key": "dummpy_key"}
if __name__ == "__main__":
from torch.utils.data import DataLoader
from diffusion.data.datasets.utils import ASPECT_RATIO_1024
from diffusion.data.transforms import get_transform
image_size = 256
transform = get_transform("default_train", image_size)
data_dir = ["data/debug_data_train/debug_data"]
for data_path in data_dir:
train_dataset = SanaWebDatasetMS(data_dir=data_path, resolution=image_size, transform=transform, max_length=300)
dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4)
for data in tqdm(dataloader):
break
print(dataloader.dataset.index_info)
|