CARN 학습 시 PSNR, SSIM eval 코드 추가

@@ -2,7 +2,7 @@ import os
 import random
 import random
 import numpy as np
 import numpy as np
 import scipy.misc as misc
 import scipy.misc as misc
-import skimage.measure as measure
+import skimage.metrics as metrics
 from tensorboardX import SummaryWriter
 from tensorboardX import SummaryWriter
 import torch
 import torch
 import torch.nn as nn
 import torch.nn as nn
@@ -92,8 +92,9 @@ class Solver():
                 self.step += 1
                 self.step += 1
                 if cfg.verbose and self.step % cfg.print_interval == 0:
                 if cfg.verbose and self.step % cfg.print_interval == 0:
                     if cfg.scale > 0:
                     if cfg.scale > 0:
-                        psnr = self.evaluate("dataset/Urban100", scale=cfg.scale, num_step=self.step)
-                        self.writer.add_scalar("Urban100", psnr, self.step)
+                        psnr, ssim = self.evaluate("dataset/Urban100", scale=cfg.scale, num_step=self.step)
+                        self.writer.add_scalar("PSNR", psnr, self.step)
+                        self.writer.add_scalar("SSIM", ssim, self.step)
                     else:
                     else:
                         psnr = [self.evaluate("dataset/Urban100", scale=i, num_step=self.step) for i in range(2, 5)]
                         psnr = [self.evaluate("dataset/Urban100", scale=i, num_step=self.step) for i in range(2, 5)]
                         self.writer.add_scalar("Urban100_2x", psnr[0], self.step)
                         self.writer.add_scalar("Urban100_2x", psnr[0], self.step)
@@ -107,6 +108,7 @@ class Solver():
     def evaluate(self, test_data_dir, scale=2, num_step=0):
     def evaluate(self, test_data_dir, scale=2, num_step=0):
         cfg = self.cfg
         cfg = self.cfg
         mean_psnr = 0
         mean_psnr = 0
+        mean_ssim = 0
         self.refiner.eval()
         self.refiner.eval()
 
 
         test_data   = TestDataset(test_data_dir, scale=scale)
         test_data   = TestDataset(test_data_dir, scale=scale)
@@ -149,15 +151,16 @@ class Solver():
             hr = hr.cpu().mul(255).clamp(0, 255).byte().permute(1, 2, 0).numpy()
             hr = hr.cpu().mul(255).clamp(0, 255).byte().permute(1, 2, 0).numpy()
             sr = sr.cpu().mul(255).clamp(0, 255).byte().permute(1, 2, 0).numpy()
             sr = sr.cpu().mul(255).clamp(0, 255).byte().permute(1, 2, 0).numpy()
 
 
-            # evaluate PSNR
+            # evaluate PSNR and SSIM
             # this evaluation is different to MATLAB version
             # this evaluation is different to MATLAB version
             # we evaluate PSNR in RGB channel not Y in YCbCR
             # we evaluate PSNR in RGB channel not Y in YCbCR
             bnd = scale
             bnd = scale
-            im1 = hr[bnd:-bnd, bnd:-bnd]
-            im2 = sr[bnd:-bnd, bnd:-bnd]
+            im1 = im2double(hr[bnd:-bnd, bnd:-bnd])
+            im2 = im2double(sr[bnd:-bnd, bnd:-bnd])
             mean_psnr += psnr(im1, im2) / len(test_data)
             mean_psnr += psnr(im1, im2) / len(test_data)
+            mean_ssim += ssim(im1, im2) / len(test_data)
 
 
-        return mean_psnr
+        return mean_psnr, mean_ssim
 
 
     def load(self, path):
     def load(self, path):
         self.refiner.load_state_dict(torch.load(path))
         self.refiner.load_state_dict(torch.load(path))
@@ -177,14 +180,15 @@ class Solver():
         lr = self.cfg.lr * (0.5 ** (self.step // self.cfg.decay))
         lr = self.cfg.lr * (0.5 ** (self.step // self.cfg.decay))
         return lr
         return lr
 
 
-
-def psnr(im1, im2):
-    def im2double(im):
+def im2double(im):
     min_val, max_val = 0, 255
     min_val, max_val = 0, 255
     out = (im.astype(np.float64)-min_val) / (max_val-min_val)
     out = (im.astype(np.float64)-min_val) / (max_val-min_val)
     return out
     return out
 
 
-    im1 = im2double(im1)
-    im2 = im2double(im2)
-    psnr = measure.compare_psnr(im1, im2, data_range=1)
+def psnr(im1, im2):
+    psnr = metrics.peak_signal_noise_ratio(im1, im2, data_range=1)
     return psnr
     return psnr
+
+def ssim(im1, im2):
+    ssim = metrics.structural_similarity(im1, im2, data_range=1, multichannel=True)
+    return ssim