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generate_with_mask.py
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generate_with_mask.py
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import argparse, os, sys, glob
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from torch.utils.data import DataLoader
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
from ldm.data.personalized import Positive_sample_with_generated_mask
import random
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
sd = pl_sd["state_dict"]
config.model.params.ckpt_path = ckpt
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def make_batch(image, mask, device):
image = np.array(Image.open(image).convert("RGB"))
image = image.astype(np.float32)/255.0
image = image[None].transpose(0,3,1,2)
image = torch.from_numpy(image).to(device)*2-1
mask = np.array(Image.open(mask).convert("L"))
mask = mask.astype(np.float32)/255.0
mask = mask[None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask).to(device)
print(image.shape, mask.shape)
batch = {"image": image, "mask": mask,}
return batch
def log_local( images,masked_img, cnt,sample_name,sample_name2,anomaly_name,ori_img=None,sub_dir=None):
root='test-results/%s'%sub_dir
for k in images:
N = images[k].shape[0]
images[k] = images[k][:N]
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().cpu()
images[k] = torch.clamp(images[k], -1., 1.)
resize = transforms.Resize(images['samples_inpainting'].size(-1))
for k in images:
continue
if k in ['samples_inpainting','mask']:
grid = torchvision.utils.make_grid(images[k], nrow=4)
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
filename = "{}-{}-{}-{:02}-2out-{}.jpg".format(sample_name,sample_name2,anomaly_name,cnt,k[:4])
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
#masked_img=resize(masked_img)
# filename = "{}-{}-{}-{:02}-0mask.jpg".format(sample_name,sample_name2,anomaly_name,cnt)
# path = os.path.join(root, filename)
# save_image(masked_img,path,nrow=masked_img.size(0))
if ori_img is not None:
ori_img=torch.cat([ori_img,images['samples_inpainting']],dim=0)
ori_img = resize(ori_img)
filename = "{}-{}-{}-{:02}-1ori.jpg".format(sample_name, sample_name2, anomaly_name, cnt)
path = os.path.join(root, filename)
save_image((ori_img+1)/2, path, nrow=masked_img.size(0))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_root",
required=True,
)
parser.add_argument(
"--sample_name",
default='capsule',
)
parser.add_argument(
"--anomaly_name",
default='crack',
)
parser.add_argument(
"--adaptive_mask",
action="store_true", default=False,
help='whether use adaptive attention reweighting',
)
# setup_seed(42)
opt = parser.parse_args()
config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-finetune-encoder+embedding.yaml")
actual_resume = './models/ldm/text2img-large/model.ckpt'
model = load_model_from_config(config, actual_resume)
sample_name=opt.sample_name
anomaly_name=opt.anomaly_name
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
model.prepare_spatial_encoder(optimze_together=True)
ckpt = torch.load('logs/anomaly-checkpoints/checkpoints/spatial_encoder.pt')
model.embedding_manager.spatial_encoder_model.load_state_dict(ckpt)
model.embedding_manager.load('logs/anomaly-checkpoints/checkpoints/embeddings.pt')
dataset = Positive_sample_with_generated_mask(opt.data_root,sample_name, anomaly_name, repeats=1, size=256, set='train',
per_image_tokens=False)
dataloader = DataLoader(dataset, batch_size=8, shuffle=False, drop_last=True)
save_dir = 'generated_dataset/%s/%s' % (sample_name, anomaly_name)
os.makedirs(save_dir,exist_ok=True)
os.makedirs(os.path.join(save_dir,'image'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'mask'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'image-mask'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'ori'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'recon'), exist_ok=True)
cnt=0
with torch.no_grad():
for epoch in range(1000):
for idx, batch in enumerate(dataloader):
if cnt>500:
exit()
with model.ema_scope():
mask=batch['mask'].cpu()
ori_images=batch['image'].permute(0,3,1,2)
images=model.log_images(batch,sample=False,inpaint=True,unconditional_only=True,adaptive_mask=opt.adaptive_mask)
imgs=images['samples_inpainting'].cpu()
recon_image=images['reconstruction']
for i in range(len(imgs)):
save_image((imgs[i] + 1) / 2, os.path.join(save_dir, 'image', '%d.jpg' % cnt), normalize=False)
save_image((ori_images[i] + 1) / 2, os.path.join(save_dir, 'ori', '%d.jpg' % cnt),
normalize=False)
save_image((recon_image[i]+1) / 2, os.path.join(save_dir, 'recon', '%d.jpg' % cnt),
normalize=False)
save_image(mask[i], os.path.join(save_dir, 'mask','%d.jpg' % cnt))
save_image(torch.stack([(imgs[i]+1)/2,mask[i].repeat(3,1,1)],dim=0), os.path.join(save_dir, 'image-mask', '%d.jpg' % cnt))
cnt+=1
#python generate_with_mask.py --sample_name=screw --anomaly_name=thread_side --adaptive_mask
#python generate_with_mask.py --sample_name=wood --anomaly_name=color --adaptive_mask