From 24d88d04b6a56e2293afdc59ae4623681fd7aaa3 Mon Sep 17 00:00:00 2001
From: GreenWizard <sizeof.2011@gmail.com>
Date: Wed, 10 Apr 2024 12:29:15 +0200
Subject: [PATCH] make R starting from 0.0

---
 NN/RestorationModel/CRestorationModel.py |  2 +-
 Utils/CroppingAugm.py                    | 28 +++++++++++++-----------
 2 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/NN/RestorationModel/CRestorationModel.py b/NN/RestorationModel/CRestorationModel.py
index 4ed4df6..6aec5c8 100644
--- a/NN/RestorationModel/CRestorationModel.py
+++ b/NN/RestorationModel/CRestorationModel.py
@@ -56,7 +56,7 @@ def _addResiduals(self, latents, residuals):
 
     return latents
 
-  def _addRadius(self, latents, R=None, fakeR=1e-5, training=False):
+  def _addRadius(self, latents, R=None, fakeR=0.0, training=False):
     B = tf.shape(latents)[0]
     if self._blurRadiusEncoder is not None:
       if R is None:
diff --git a/Utils/CroppingAugm.py b/Utils/CroppingAugm.py
index 395994e..d5cddf6 100644
--- a/Utils/CroppingAugm.py
+++ b/Utils/CroppingAugm.py
@@ -36,6 +36,7 @@ def SubsampleProcessor(target_crop_size, N, extras=[], sampler='uniform'):
   withBlur = blurConfig is not None
   if withBlur:
     blurRange = blurConfig['min'] + tf.linspace(0.0, blurConfig['max'], blurConfig['N'])
+    minR = tf.reduce_min(blurRange)
     blurN = tf.size(blurRange)
     blurShared = blurConfig.get('shared', False)
     if blurShared:
@@ -60,20 +61,21 @@ def _FF(img):
         sobel = extractInterpolated(sobel, positions)
         res['sobel'] = tf.reshape(sobel, [N, 6])
 
-      if withBlur and blurShared:
-        idx = tf.random.uniform((1,), minval=0, maxval=blurN, dtype=tf.int32)
-        R = tf.gather(blurRange, idx)
-        R = tf.reshape(R, (1,))
+      if withBlur:
+        if blurShared:
+          idx = tf.random.uniform((1,), minval=0, maxval=blurN, dtype=tf.int32)
+          R = tf.gather(blurRange, idx)
+          R = tf.reshape(R, (1,))
+          R = tf.fill([N, 1], R[0])
+        else:
+          idx = tf.random.uniform((N,), minval=0, maxval=blurN, dtype=tf.int32)
+          R = tf.gather(blurRange, idx)
+          R = tf.reshape(R, (N, 1))
+          pass
+
+        tf.assert_equal(tf.shape(R), (N, 1))
         res['blured'] = blur(src, positions[0], R)
-        res['blur R'] = tf.fill([N, 1], R[0])
-        pass
-
-      if withBlur and not blurShared:
-        idx = tf.random.uniform((N,), minval=0, maxval=blurN, dtype=tf.int32)
-        R = tf.gather(blurRange, idx)
-        R = tf.reshape(R, (N, 1))
-        res['blured'] = blur(src, positions[0], R)
-        res['blur R'] = R
+        res['blur R'] = R - minR # ensure that R is starting from 0.0
         pass
       return res
     return _FF