Spaces:
Sleeping
Sleeping
File size: 7,075 Bytes
28c6826 |
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 |
from torch.utils.data import Dataset
import os
import numpy as np
import taming.models.vqgan
import open_clip
import random
from PIL import Image
import torch
import math
import json
import torchvision.transforms as transforms
torch.manual_seed(0)
np.random.seed(0)
class test_custom_dataset(Dataset):
def __init__(self, style: str = None):
self.empty_context = np.load("assets/contexts/empty_context.npy")
self.object=[
"A chihuahua ",
"A tabby cat ",
"A portrait of chihuahua ",
"An apple on the table ",
"A banana on the table ",
"A church on the street ",
"A church in the mountain ",
"A church in the field ",
"A church on the beach ",
"A chihuahua walking on the street ",
"A tabby cat walking on the street",
"A portrait of tabby cat ",
"An apple on the dish ",
"A banana on the dish ",
"A human walking on the street ",
"A temple on the street ",
"A temple in the mountain ",
"A temple in the field ",
"A temple on the beach ",
"A chihuahua walking in the forest ",
"A tabby cat walking in the forest ",
"A portrait of human face ",
"An apple on the ground ",
"A banana on the ground ",
"A human walking in the forest ",
"A cabin on the street ",
"A cabin in the mountain ",
"A cabin in the field ",
"A cabin on the beach ",
]
self.style = [
"in 3d rendering style",
]
if style is not None:
self.style = [style]
def __getitem__(self, index):
prompt = self.object[index]+self.style[0]
return prompt, prompt
def __len__(self):
return len(self.object)
def unpreprocess(self, v): # to B C H W and [0, 1]
v.clamp_(0., 1.)
return v
@property
def fid_stat(self):
return f'assets/fid_stats/fid_stats_cc3m_val.npz'
class train_custom_dataset(Dataset):
def __init__(self, train_file: str=None, ):
self.train_img = json.load(open(train_file, 'r'))
self.path_preffix = "/".join(train_file.split("/")[:-1])
self.prompt = []
self.image = []
self.style = []
for im in self.train_img.keys():
im_path = os.path.join(self.path_preffix, im)
self.object = self.train_img[im][0]
self.style = self.train_img[im][1]
im_prompt = self.object +" "+self.style
self.image.append(im_path)
self.prompt.append(im_prompt)
self.empty_context = np.load("assets/contexts/empty_context.npy")
self.transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),
transforms.ToTensor(),
])
print("-----------------"*3)
print("train dataset length: ", len(self.prompt))
print("train dataset length: ", len(self.image))
print(self.prompt[0])
print(self.image[0])
print("-----------------"*3)
def __getitem__(self, index):
prompt = self.prompt[0]
image = Image.open(self.image[0]).convert("RGB")
image = self.transform(image)
return image,prompt
# return dict(img=image_embedding, text=text_embedding)
def __len__(self):
return 24
def unpreprocess(self, v): # to B C H W and [0, 1]
v.clamp_(0., 1.)
return v
@property
def fid_stat(self):
return f'assets/fid_stats/fid_stats_cc3m_val.npz'
class Discriptor(Dataset):
def __init__(self,style: str=None):
self.object =[
# "A parrot ",
# "A bird ",
# "A chihuahua in the snow",
# "A towel ",
# "A number '1' ",
# "A number '2' ",
# "A number '3' ",
# "A number '6' ",
# "A letter 'L' ",
# "A letter 'Z' ",
# "A letter 'D' ",
# "A rabbit ",
# "A train ",
# "A table ",
# "A dish ",
# "A large boat ",
# "A puppy ",
# "A cup ",
# "A watermelon ",
# "An apple ",
# "A banana ",
# "A chair ",
# "A Welsh Corgi ",
# "A cat ",
# "A house ",
# "A flower ",
# "A sunflower ",
# "A car ",
# "A jeep car ",
# "A truck ",
# "A Posche car ",
# "A vase ",
# "A chihuahua ",
# "A tabby cat ",
"A portrait of chihuahua ",
"An apple on the table ",
"A banana on the table ",
"A human ",
"A church on the street ",
"A church in the mountain ",
"A church in the field ",
"A church on the beach ",
"A chihuahua walking on the street ",
"A tabby cat walking on the street",
"A portrait of tabby cat ",
"An apple on the dish ",
"A banana on the dish ",
"A human walking on the street ",
"A temple on the street ",
"A temple in the mountain ",
"A temple in the field ",
"A temple on the beach ",
"A chihuahua walking in the forest ",
"A tabby cat walking in the forest ",
"A portrait of human face ",
"An apple on the ground ",
"A banana on the ground ",
"A human walking in the forest ",
"A cabin on the street ",
"A cabin in the mountain ",
"A cabin in the field ",
"A cabin on the beach ",
"A letter 'A' ",
"A letter 'B' ",
"A letter 'C' ",
"A letter 'D' ",
"A letter 'E' ",
"A letter 'F' ",
"A letter 'G' ",
"A butterfly ",
" A baby penguin ",
"A bench ",
"A boat ",
"A cow ",
"A hat ",
"A piano ",
"A robot ",
"A christmas tree ",
"A dog ",
"A moose ",
]
self.style =[
"in 3d rendering style",
]
if style is not None:
self.style = [style]
def __getitem__(self, index):
prompt = self.object[index]+self.style[0]
return prompt
def __len__(self):
return len(self.object)
def unpreprocess(self, v): # to B C H W and [0, 1]
v.clamp_(0., 1.)
return v
@property
def fid_stat(self):
return f'assets/fid_stats/fid_stats_cc3m_val.npz'
|