File size: 9,156 Bytes
424919d | 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 | """
Modified from guided-diffusion/scripts/image_sample.py
"""
import argparse
import os
import torch
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from dataset import TMDistilDireDataset
from torch.utils.data import DataLoader
import cv2
import torch.nn.functional as F
import torchvision.transforms as transforms
from tqdm.auto import tqdm
import numpy as np
import torch as th
import torchvision
import os.path as osp
from .guided_diffusion.script_util import (
NUM_CLASSES,
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
dict_parse,
args_to_dict,
)
def reshape_image(imgs: torch.Tensor, image_size: int) -> torch.Tensor:
if len(imgs.shape) == 3:
imgs = imgs.unsqueeze(0)
if imgs.shape[2] != imgs.shape[3]:
crop_func = transforms.CenterCrop(image_size)
imgs = crop_func(imgs)
if imgs.shape[2] != image_size:
imgs = F.interpolate(imgs, size=(image_size, image_size), mode="bicubic", antialias=True)
return imgs
def create_argparser():
defaults = dict(
clip_denoised=True,
num_samples=-1,
use_ddim=True,
real_step=0,
continue_reverse=False,
has_subfolder=False,
)
defaults.update(model_and_diffusion_defaults())
sanic_dict = dict(
attention_resolutions='32,16,8',
class_cond=False,
diffusion_steps=1000,
image_size=256,
learn_sigma=True,
model_path="./models/256x256-adm.pt",
noise_schedule='linear',
num_channels=256,
num_head_channels=64,
num_res_blocks=2,
resblock_updown=True,
use_fp16=True,
use_scale_shift_norm=True,
data_root="",
compute_dire=False,
compute_eps=False,
save_root="",
batch_size=32,
)
defaults.update(sanic_dict)
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
args = parser.parse_args()
return args_to_dict(args, list(defaults.keys()))
def create_dicts_for_static_init():
defaults = dict(
clip_denoised=True,
num_samples=-1,
use_ddim=True,
real_step=0,
continue_reverse=False,
has_subfolder=False,
)
defaults.update(model_and_diffusion_defaults())
sanic_dict = dict(
attention_resolutions='32,16,8',
class_cond=False,
diffusion_steps=1000,
image_size=256,
learn_sigma=True,
model_path="./models/256x256-adm.pt",
noise_schedule='linear',
num_channels=256,
num_head_channels=64,
num_res_blocks=2,
resblock_updown=True,
use_fp16=True,
use_scale_shift_norm=True,
data_root="",
compute_dire=False,
compute_eps=False,
save_root="",
batch_size=32,
)
defaults.update(sanic_dict)
return defaults
@torch.no_grad()
def dire(img_batch:torch.Tensor, model, diffusion, args, save_img=False, save_path=None):
print("computing recons & DIRE ...")
imgs = img_batch.cuda()
batch_size = imgs.shape[0]
model_kwargs = {}
reverse_fn = diffusion.ddim_reverse_sample_loop
assert (imgs.shape[2] == imgs.shape[3]) and (imgs.shape[3] == args['image_size']), f"Image size mismatch: {imgs.shape[2]} != {args['image_size']}"
latent = reverse_fn(
model,
(batch_size, 3, args['image_size'], args['image_size']),
noise=imgs,
clip_denoised=args['clip_denoised'],
model_kwargs=model_kwargs,
real_step=args['real_step'],
)
sample_fn = diffusion.p_sample_loop if not args['use_ddim'] else diffusion.ddim_sample_loop
recons = sample_fn(
model,
(batch_size, 3, args['image_size'], args['image_size']),
noise=latent,
clip_denoised=args['clip_denoised'],
model_kwargs=model_kwargs,
real_step=args['real_step']
)
dire = th.abs(imgs - recons)
dire = (dire*255./2).clamp(0, 255).to(th.uint8)
dire = dire.contiguous() / 255.
dire = (dire).clamp(0, 1).to(th.float32)
# scale imgs and recons
imgs = (imgs+1)*0.5
recons = (recons+1)*0.5
if save_img:
# save images
for i in range(len(img_batch)):
dire_img = dire[i].detach().cpu().numpy().transpose(1, 2, 0)
dire_img = cv2.cvtColor(dire_img, cv2.COLOR_RGB2BGR)
dire_path = os.path.join(save_path, f"dire_{i}.png")
cv2.imwrite(dire_path, dire_img*255)
return dire, imgs, recons
@torch.no_grad()
def dire_get_first_step_noise(img_batch:torch.Tensor, model, diffusion, args, device):
imgs = img_batch.to(device)
batch_size = imgs.shape[0]
model_kwargs = {}
reverse_fn = diffusion.ddim_reverse_sample_only_eps
assert (imgs.shape[2] == imgs.shape[3]) and (imgs.shape[3] == args['image_size']), f"Image size mismatch: {imgs.shape[2]} != {args['image_size']}"
eps = reverse_fn(
model,
# (batch_size, 3, args['image_size'], args['image_size']),
x=imgs,
t=torch.zeros(imgs.shape[0],).long().to(device),
clip_denoised=args['clip_denoised'],
model_kwargs=model_kwargs,
eta=0.0
#real_step=args['real_step'],
)
return eps
if __name__ == "__main__":
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
import os
dist.init_process_group(backend='nccl', init_method='env://')
local_rank = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
# Set device for this process
device = torch.device("cuda")
adm_args = create_argparser()
adm_args['timestep_respacing'] = 'ddim20'
adm_model, diffusion = create_model_and_diffusion(**dict_parse(adm_args, model_and_diffusion_defaults().keys()))
print(f"checkpoint: {adm_args['model_path']}")
print(f"model channel: {adm_args['num_channels']}, {adm_model.model_channels}")
adm_model.load_state_dict(torch.load(adm_args['model_path'], map_location="cpu"))
adm_model.to(device)
adm_model.convert_to_fp16()
adm_model.eval()
dataset = TMDistilDireDataset(adm_args['data_root'], prepared_dire=False)
sampler = DistributedSampler(dataset, shuffle=False)
os.makedirs(osp.join(adm_args['save_root'], 'images', 'fakes'), exist_ok=True)
os.makedirs(osp.join(adm_args['save_root'], 'images', 'reals'), exist_ok=True)
os.makedirs(osp.join(adm_args['save_root'], 'dire', 'fakes'), exist_ok=True)
os.makedirs(osp.join(adm_args['save_root'], 'dire', 'reals'), exist_ok=True)
os.makedirs(osp.join(adm_args['save_root'], 'eps', 'fakes'), exist_ok=True)
os.makedirs(osp.join(adm_args['save_root'], 'eps', 'reals'), exist_ok=True)
print(f"Dataset length: {len(dataset)}")
dataloader = DataLoader(dataset, batch_size=adm_args['batch_size'], num_workers=4, drop_last=False, pin_memory=True, sampler=sampler)#
for (img_batch, dire_batch, eps_batch, isfake_batch), (img_pathes, dire_pathes, eps_pathes) in tqdm(dataloader):
haveall=True
for i in range(len(img_batch)):
basename = osp.basename(img_pathes[i])
isfake = isfake_batch[i]
img_path = osp.join(adm_args['save_root'], 'images', 'fakes', basename) if isfake else osp.join(adm_args['save_root'], 'images', 'reals', basename)
dire_path = img_path.replace('/images/', '/dire/')
eps_path = img_path.replace('/images/', '/eps/').split('.')[0] + '.pt'
if (not osp.exists(img_pathes[i])) or (not osp.exists(dire_path)) or (not osp.exists(eps_path)):
haveall=False
break
if haveall:
continue
with torch.no_grad():
eps = None
img = (img_batch.detach().cpu()+1)*0.5
if adm_args['compute_eps']:
eps = dire_get_first_step_noise(img_batch, adm_model, diffusion, adm_args, device)
eps = eps.detach().cpu()
if adm_args['compute_dire']:
dire_img, img, recons = dire(img_batch, adm_model, diffusion, adm_args)
dire_img = dire_img.detach().cpu()
img = img.detach().cpu()
for i in range(len(img_batch)):
basename = osp.basename(img_pathes[i])
isfake = isfake_batch[i]
img_path = osp.join(adm_args['save_root'], 'images', 'fakes', basename) if isfake else osp.join(adm_args['save_root'], 'images', 'reals', basename)
dire_path = img_path.replace('/images/', '/dire/')
eps_path = img_path.replace('/images/', '/eps/').split('.')[0] + '.pt'
if not osp.exists(img_path):
torchvision.utils.save_image(img[i], img_path)
if not osp.exists(dire_path) and adm_args['compute_dire']:
torchvision.utils.save_image(dire_img[i], dire_path)
if not osp.exists(eps_path) and eps is not None:
torch.save(eps[i], eps_path) |