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2D_random_points.py
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2D_random_points.py
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import torch, os, logging
from argparse import ArgumentParser
import numpy as np
from matplotlib import pyplot as plt
from trainer import *
from utils import *
from MLPs import *
to8b = lambda x : (255*np.clip(x,0,1)).astype(np.uint8)
def get_logger(filename, verbosity=1, name=None):
level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
formatter = logging.Formatter(
"[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
)
logger = logging.getLogger(name)
logger.setLevel(level_dict[verbosity])
fh = logging.FileHandler(filename, "w")
fh.setFormatter(formatter)
logger.addHandler(fh)
sh = logging.StreamHandler()
sh.setFormatter(formatter)
logger.addHandler(sh)
return logger
def main(args):
if os.path.exists(args.save_path):
print('Path already exists!')
return 1
os.mkdir(args.save_path)
logger = get_logger(args.save_path+args.logger)
logger.info(args)
# Set the CUDA flag
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info('device is: {}'.format(device))
buf = np.load(args.data_path)
#mask = np.load(args.mask_path)
# ########### generate random mask ###########
image_size = buf['test_data'].shape[1]
ratio = 0.25
mask = []
for i in range(buf['test_data'].shape[0]):
mask_tmp = []
for j in range(args.N_repeat):
idx = torch.randperm(image_size**2)[:int(ratio*image_size**2)]
mask_np_N2 = np.zeros((image_size**2))
mask_np_N2[idx] = 1
mask_np_N2 = mask_np_N2==1
mask_tmp.append(mask_np_N2)
mask.append(np.stack(mask_tmp,0))
mask = np.stack(mask,0)
#np.save('mask_2d_{}_{}_{}_{}.npy'.format(ratio,buf.shape[0],args.N_repeat,buf.shape[1]),mask)
# ###########################################
signals = torch.from_numpy(buf['test_data']/ 255.)
logger.info('################ Simple Encoding ################')
ez = 512
rff_params = [8]
linearf_params = [4]
logf_params = [4.5]
gaussian4_params = [0.006]
linear4_params = [2.5/128]
encoding_methods = ['RFF']*len(rff_params)+['LinF']*len(linearf_params)+['LogF']*len(logf_params)+['Gau']*len(gaussian4_params)+['Tri']*len(linear4_params)
params = rff_params+linearf_params+logf_params+gaussian4_params+linear4_params
print(encoding_methods)
print(params)
for depth in [4,1,0]:
for lr in [5e-3]:
logger.info('######## Network Depth = {}, Learning Rate = {} ########'.format(depth,lr))
for em,param in zip(encoding_methods,params):
ef = encoding_func_2D(em,[param,ez])
time_,trn_psnr_,tst_psnr_,rec_ = train_random_simple_2D(signals,ef,mask=mask,N_repeat=args.N_repeat,lr=lr,epochs=2000,depth=depth,device=device,logger=None)
file_name = 'RD{}{}'.format(depth,em)
if args.save_flag:
# np.save(args.save_path+file_name+'_rec.npy',rec_)
# np.save(args.save_path+file_name+'_time.npy',time_)
# np.save(args.save_path+file_name+'_trn.npy',trn_psnr_)
# np.save(args.save_path+file_name+'_tst.npy',tst_psnr_)
for i in range(signals.shape[0]):
plt.imshow(rec_[i])
plt.axis('off')
plt.savefig(args.save_path+'I{}'.format(i)+file_name+'{}.pdf'.format(tst_psnr_[i,-1]), bbox_inches='tight', pad_inches=0)
logger.info('embedding method:{}, param:{}, psnr:{}, std:{}, time:{}.'.format(em,param,np.mean(tst_psnr_[:,:]),np.std(tst_psnr_[:,:]),np.mean(time_[:,:])))
logger.info('################ Complex Encoding ################')
ez = 256
rff_params = [30]
linearf_params = [4.5]
logf_params = [6]
gaussian_params = [0.005]
linear_params = [2.5/256]
encoding_methods = ['RFF']*len(rff_params)+['LinF']*len(linearf_params)+['LogF']*len(logf_params)+['Gau']*len(gaussian_params)+['Tri']*len(linear_params)
params = rff_params+linearf_params+logf_params+gaussian_params+linear_params
for depth in [0,1]:
for lr in [1e-1]:
logger.info('######## Network Depth = {}, Learning Rate = {} ########'.format(depth,lr))
for em,param in zip(encoding_methods,params):
ef = encoding_func_1D(em,[param,ez])
bl = blending_func_2D(ef)
time_,trn_psnr_,tst_psnr_,rec_ = train_index_blend_kron_2D(signals,bl,ef,mask=mask,N_repeat=args.N_repeat,lr=lr,epochs=2000,depth=depth,device=device,logger=None)
file_name = 'RKD{}{}'.format(depth,em)
if args.save_flag:
# np.save(args.save_path+file_name+'_rec.npy',rec_)
# np.save(args.save_path+file_name+'_time.npy',time_)
# np.save(args.save_path+file_name+'_trn.npy',trn_psnr_)
# np.save(args.save_path+file_name+'_tst.npy',tst_psnr_)
for i in range(signals.shape[0]):
plt.imshow(rec_[i])
plt.axis('off')
plt.savefig(args.save_path+'I{}'.format(i)+file_name+'{}.pdf'.format(tst_psnr_[i,-1]), bbox_inches='tight', pad_inches=0)
logger.info('embedding method:{}, param:{}, psnr:{}, std:{}, time:{}.'.format(em,param,np.mean(tst_psnr_[:,:]),np.std(tst_psnr_[:,:]),np.mean(time_[:,:])))
if __name__ == "__main__":
torch.set_default_dtype(torch.float32)
torch.manual_seed(20220222)
np.random.seed(20220222)
parser = ArgumentParser()
parser.add_argument("--data_path", type=str, default="data_div2k.npz")
parser.add_argument("--mask_path", type=str, default="mask_2d_0.25_16_1_512.npy")
parser.add_argument("--N_repeat", type=int, default=1)
parser.add_argument("--save_path", type=str, default="2D_random_points/")
parser.add_argument("--logger", type=str, default="log.log")
parser.add_argument("--save_flag", type=int, default=0, choices=[0, 1])
args = parser.parse_args()
main(args)