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| import numpy as np |
| import torch |
| import clip |
| from PIL import Image |
| import copy |
| from manipulate import Manipulator |
| import argparse |
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| 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,:] |
| 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 |
|
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| |
| 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] |
| M.step=2 |
| M.num_images=num_images |
| select=np.array(M.mindexs)<=16 |
| mindexs2=np.array(M.mindexs)[select] |
| for lindex in mindexs2: |
| 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) |
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| file_path='./npy/'+M.dataset_name+'/' |
| np.save(file_path+'fs3',fs3) |
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