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Running
on
Zero
File size: 5,609 Bytes
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import os
import random
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
from transformers import AutoImageProcessor
from DiT_VAE.diffusion.data.builder import DATASETS
from omegaconf import OmegaConf
from torchvision import transforms
from transformers import CLIPImageProcessor
import io
import zipfile
import numpy
import json
def to_rgb_image(maybe_rgba: Image.Image):
if maybe_rgba.mode == 'RGB':
return maybe_rgba
elif maybe_rgba.mode == 'RGBA':
rgba = maybe_rgba
img = numpy.random.randint(127, 128, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8)
img = Image.fromarray(img, 'RGB')
img.paste(rgba, mask=rgba.getchannel('A'))
return img
else:
raise ValueError("Unsupported image type.", maybe_rgba.mode)
@DATASETS.register_module()
class TriplaneData(Dataset):
def __init__(self,
data_base_dir,
model_names,
data_json_file,
dino_path,
i_drop_rate=0.1,
image_size=256,
**kwargs):
self.dict_data_image = json.load(open(data_json_file)) # {'image_name': pose}
self.data_base_dir = data_base_dir
self.dino_img_processor = AutoImageProcessor.from_pretrained(dino_path)
self.size = image_size
self.data_list = list(self.dict_data_image.keys())
self.zip_file_dict = {}
config_gan_model = OmegaConf.load(model_names)
all_models = config_gan_model['gan_models'].keys()
for model_name in all_models:
zipfile_path = os.path.join(self.data_base_dir, model_name + '.zip')
zipfile_load = zipfile.ZipFile(zipfile_path)
self.zip_file_dict[model_name] = zipfile_load
self.transform = transforms.Compose([
transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(self.size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
])
self.clip_image_processor = CLIPImageProcessor()
self.i_drop_rate = i_drop_rate
def getdata(self, idx):
data_name = self.data_list[idx]
data_model_name = self.dict_data_image[data_name]['model_name']
zipfile_loaded = self.zip_file_dict[data_model_name]
# zipfile_path = os.path.join(self.data_base_dir, data_model_name)
# zipfile_loaded = zipfile.ZipFile(zipfile_path)
with zipfile_loaded.open(self.dict_data_image[data_name]['z_dir'], 'r') as f:
buffer = io.BytesIO(f.read())
data_z = torch.load(buffer)
with zipfile_loaded.open(self.dict_data_image[data_name]['vert_dir'], 'r') as f:
buffer = io.BytesIO(f.read())
data_vert = torch.load(buffer)
with zipfile_loaded.open(self.dict_data_image[data_name]['img_dir'], 'r') as f:
raw_image = to_rgb_image(Image.open(f))
dino_img = self.dino_img_processor(images=raw_image, return_tensors="pt").pixel_values
image = self.transform(raw_image.convert("RGB"))
clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
drop_image_embed = 0
rand_num = random.random()
if rand_num < self.i_drop_rate:
drop_image_embed = 1
return {
"raw_image": raw_image,
"dino_img": dino_img,
"image": image,
"clip_image": clip_image.clone(),
"data_z": data_z,
"data_vert": data_vert,
"data_model_name": data_model_name,
"drop_image_embed": drop_image_embed,
}
#
# img_path = self.img_samples[index]
# npz_path = self.txt_feat_samples[index]
# npy_path = self.vae_feat_samples[index]
# prompt = self.prompt_samples[index]
# data_info = {
# 'img_hw': torch.tensor([torch.tensor(self.resolution), torch.tensor(self.resolution)], dtype=torch.float32),
# 'aspect_ratio': torch.tensor(1.)
# }
#
# img = self.loader(npy_path) if self.load_vae_feat else self.loader(img_path)
# txt_info = np.load(npz_path)
# txt_fea = torch.from_numpy(txt_info['caption_feature']) # 1xTx4096
# attention_mask = torch.ones(1, 1, txt_fea.shape[1]) # 1x1xT
# if 'attention_mask' in txt_info.keys():
# attention_mask = torch.from_numpy(txt_info['attention_mask'])[None]
# if txt_fea.shape[1] != self.max_lenth:
# txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_lenth-txt_fea.shape[1], 1)], dim=1)
# attention_mask = torch.cat([attention_mask, torch.zeros(1, 1, self.max_lenth-attention_mask.shape[-1])], dim=-1)
#
# if self.transform:
# img = self.transform(img)
#
# data_info['prompt'] = prompt
# return img, txt_fea, attention_mask, data_info
def __getitem__(self, idx):
for _ in range(20):
try:
return self.getdata(idx)
except Exception as e:
print(f"Error details: {str(e)}")
idx = np.random.randint(len(self))
raise RuntimeError('Too many bad data.')
def __len__(self):
return len(self.data_list)
def __getattr__(self, name):
if name == "set_epoch":
return lambda epoch: None
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
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