limit blur kernel size

NN/utils_bluring.py

@@ -77,15 +77,19 @@ def f(img, points, ptR):
     return blured
   return f
 
-def extractBluredOne():
+def extractBluredOne(maxR=None):
+  if maxR is None:
+    getMaxR = lambda R: tf.cast(tf.reduce_max(R), tf.int32) + 1
+  else:
+    getMaxR = lambda R: maxR
+
   def f(img, points, R):
     img = img[None]
     tf.assert_rank(img, 4)
     B = tf.shape(points)[0]
     tf.assert_equal(tf.shape(points), (B, 2))
     R = tf.reshape(R, (1, ))
-    maxR = tf.cast(R, tf.int32) + 1
-    gaussians = gaussian_kernel(maxR[0], R)
+    gaussians = gaussian_kernel(getMaxR(R), R)
     blured = applyBluring(img, gaussians)
     gaussiansN = tf.shape(gaussians)[-1]
     tf.assert_equal(tf.shape(blured), (gaussiansN, tf.shape(img)[1], tf.shape(img)[2], 3))

Utils/CroppingAugm.py

@@ -40,7 +40,7 @@ def SubsampleProcessor(target_crop_size, N, extras=[], sampler='uniform'):
     blurN = tf.size(blurRange)
     blurShared = blurConfig.get('shared', False)
     if blurShared:
-      blur = extractBluredOne()
+      blur = extractBluredOne(maxR=blurConfig.get('max kernel', None))
     else:
       blur = extractBlured(blurRange)
     pass

configs/experiments/blur/basic-shared.json

@@ -4,6 +4,7 @@
       "blur": {
         "min": 1e-5,
         "max": 8,
+        "max kernel": 8,
         "N": 100,
         "shared": true
       }

configs/experiments/blur/basic.json

@@ -4,6 +4,7 @@
       "blur": {
         "min": 1e-5,
         "max": 8,
+        "max kernel": 8,
         "N": 100,
         "shared": false
       }