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