From f06ff1eae8ded0969803cc2346e00ba9adb4d8bc Mon Sep 17 00:00:00 2001
From: jh <jmhummel@gmail.com>
Date: Fri, 12 Mar 2021 07:57:09 -0800
Subject: [PATCH] feat: ms_ssim implementation

---
 core/leras/ops/__init__.py | 33 +++++++++++++++++++++++++++++++--
 1 file changed, 31 insertions(+), 2 deletions(-)

diff --git a/core/leras/ops/__init__.py b/core/leras/ops/__init__.py
index 500a22a..af59c6c 100644
--- a/core/leras/ops/__init__.py
+++ b/core/leras/ops/__init__.py
@@ -307,6 +307,35 @@ def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03
 
 nn.dssim = dssim
 
+
+def ms_ssim(img1, img2, resolution, kernel_size=11, k1=0.01, k2=0.03, max_value=1.0,
+            power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)):
+    img_dtype = img1.dtype
+    if img_dtype != img2.dtype:
+        raise ValueError("img1.dtype != img2.dtype")
+
+    if img_dtype != tf.float32:
+        img1 = tf.cast(img1, tf.float32)
+        img2 = tf.cast(img2, tf.float32)
+
+    # restrict mssim factors to those greater/equal to kernel size
+    power_factors = [power_factors[i] for i in range(len(power_factors)) if resolution//(2**i) >= kernel_size]
+
+    # normalize power factors if reduced because of size
+    if sum(power_factors) < 1.0:
+        power_factors = [x/sum(power_factors) for x in power_factors]
+
+    ms_ssim_val = tf.image.ssim_multiscale(img1, img2, max_value=max_value, power_factors=power_factors,
+                                           filter_size=kernel_size, k1=k1, k2=k2)
+    loss = (1.0 - ms_ssim_val) / 2.0
+
+    if img_dtype != tf.float32:
+        loss = tf.cast(loss, img_dtype)
+    return loss
+
+nn.dssim = ms_ssim
+
+
 def space_to_depth(x, size):
     if nn.data_format == "NHWC":
         # match NCHW version in order to switch data_format without problems
@@ -385,7 +414,7 @@ def total_variation_mse(images):
     """
     pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
     pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
-    
+
     tot_var = ( tf.reduce_sum(tf.square(pixel_dif1), axis=[1,2,3]) +
                 tf.reduce_sum(tf.square(pixel_dif2), axis=[1,2,3]) )
     return tot_var
@@ -400,4 +429,4 @@ def tf_suppress_lower_mean(t, eps=0.00001):
     q = tf.clip_by_value(q-t_mean_eps, 0, eps)
     q = q * (t/eps)
     return q
-"""
\ No newline at end of file
+"""