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Update dependencies due to deprecated functions in package "skimage" #1

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Update dependencies due to deprecated functions in package "skimage" #1

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90628e5
exchange deprecated functions from skimage package
niruc100 May 13, 2021
bdda7e4
exchange deprecated functions from skimage package in test_abs.py
niruc100 May 13, 2021
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6 changes: 3 additions & 3 deletions test_abs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
from skimage.measure import compare_ssim,compare_psnr, compare_mse
from skimage.metrics import structural_similarity, mean_squared_error
from utils import compute_psnr

from dataset import *
Expand Down Expand Up @@ -73,9 +73,9 @@ def test_abs(n_test,n_batch,n_steps,alpha,u,x_test):
x_test_rec[epoch_idx[iters*n_batch:np.min([(iters+1)*n_batch,n_test])],:,:,:] = x_est.cpu().detach().numpy()


mse_list = [compare_mse(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
mse_list = [mean_squared_error(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
psnr_list = [compute_psnr(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
ssim_list = [compare_ssim(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
ssim_list = [structural_similarity(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
print(f'mse {np.mean(mse_list):.2f}')
print(f'psnr {np.mean(psnr_list):.2f}')
print(f'ssim {np.mean(ssim_list):.2f}')
Expand Down
7 changes: 4 additions & 3 deletions train_abs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
from skimage.measure import compare_ssim,compare_psnr, compare_mse
from skimage.metrics import peak_signal_noise_ratio

from pathlib import Path
from dataset import *
Expand All @@ -18,7 +18,7 @@ def train_abs(n_epoch,n_train,n_batch,alpha,lr_u,n_steps,U_range,dataset,x_train
_, height, width, nc = x_train.shape

zeropad = nn.ZeroPad2d(height//2)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

dirs = f'./output_abs/20200202_{dataset}_{n_epoch}_{n_train//n_batch}_{n_batch}_{alpha}_{lr_u}_{n_steps}_{U_range}/'
disk_dir = Path(dirs)
Expand Down Expand Up @@ -141,7 +141,8 @@ def train_abs(n_epoch,n_train,n_batch,alpha,lr_u,n_steps,U_range,dataset,x_train
psnr = 20*np.log10((np.max(x_train)-np.min(x_train))/np.sqrt(mse))
print(f'psnr {psnr}')
print(f'mse {mse}')
psnr_list = [compare_psnr(x_train[i,0,:,:],x_train_rec[i,0,:,:]) for i in range(n_train)]
psnr_list = [peak_signal_noise_ratio(x_train[i,0,:,:],x_train_rec[i,0,:,:]) for i in range(n_train)]




Expand Down