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| import numpy as np | |
| import torch | |
| import clip | |
| from PIL import Image | |
| import copy | |
| from manipulate import Manipulator | |
| import argparse | |
| def GetImgF(out,model,preprocess): | |
| imgs=out | |
| imgs1=imgs.reshape([-1]+list(imgs.shape[2:])) | |
| tmp=[] | |
| for i in range(len(imgs1)): | |
| img=Image.fromarray(imgs1[i]) | |
| image = preprocess(img).unsqueeze(0).to(device) | |
| tmp.append(image) | |
| image=torch.cat(tmp) | |
| with torch.no_grad(): | |
| image_features = model.encode_image(image) | |
| image_features1=image_features.cpu().numpy() | |
| image_features1=image_features1.reshape(list(imgs.shape[:2])+[512]) | |
| return image_features1 | |
| def GetFs(fs): | |
| tmp=np.linalg.norm(fs,axis=-1) | |
| fs1=fs/tmp[:,:,:,None] | |
| fs2=fs1[:,:,1,:]-fs1[:,:,0,:] # 5*sigma - (-5)* sigma | |
| fs3=fs2/np.linalg.norm(fs2,axis=-1)[:,:,None] | |
| fs3=fs3.mean(axis=1) | |
| fs3=fs3/np.linalg.norm(fs3,axis=-1)[:,None] | |
| return fs3 | |
| #%% | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description='Process some integers.') | |
| parser.add_argument('--dataset_name',type=str,default='cat', | |
| help='name of dataset, for example, ffhq') | |
| args = parser.parse_args() | |
| dataset_name=args.dataset_name | |
| #%% | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model, preprocess = clip.load("ViT-B/32", device=device) | |
| #%% | |
| M=Manipulator(dataset_name=dataset_name) | |
| np.set_printoptions(suppress=True) | |
| print(M.dataset_name) | |
| #%% | |
| img_sindex=0 | |
| num_images=100 | |
| dlatents_o=[] | |
| tmp=img_sindex*num_images | |
| for i in range(len(M.dlatents)): | |
| tmp1=M.dlatents[i][tmp:(tmp+num_images)] | |
| dlatents_o.append(tmp1) | |
| #%% | |
| all_f=[] | |
| M.alpha=[-5,5] #ffhq 5 | |
| M.step=2 | |
| M.num_images=num_images | |
| select=np.array(M.mindexs)<=16 #below or equal to 128 resolution | |
| mindexs2=np.array(M.mindexs)[select] | |
| for lindex in mindexs2: #ignore ToRGB layers | |
| print(lindex) | |
| num_c=M.dlatents[lindex].shape[1] | |
| for cindex in range(num_c): | |
| M.dlatents=copy.copy(dlatents_o) | |
| M.dlatents[lindex][:,cindex]=M.code_mean[lindex][cindex] | |
| M.manipulate_layers=[lindex] | |
| codes,out=M.EditOneC(cindex) | |
| image_features1=GetImgF(out,model,preprocess) | |
| all_f.append(image_features1) | |
| all_f=np.array(all_f) | |
| fs3=GetFs(all_f) | |
| #%% | |
| file_path='./npy/'+M.dataset_name+'/' | |
| np.save(file_path+'fs3',fs3) | |