gt values for debugging purposes

NN/Nerf2D.py

@@ -137,7 +137,12 @@ def test_step(self, images):
     src = ensure4d(src)
     dest = ensure4d(dest)
     # call the model itself to obtain the reconstructed image in the proper format
-    reconstructed = self(src, size=tf.shape(dest)[1], training=False)
+    # add original data for debugging purposes
+    B = tf.shape(src)[0]
+    coords = generateSquareGrid(tf.shape(dest)[1], 1.0, 0.0)
+    coords = tf.tile(coords[None], [B, 1, 1])
+    values = extractInterpolated(dest, coords)
+    reconstructed = self(src, size=tf.shape(dest)[1], training=False, GT=values)
     return self._testMetrics(dest, reconstructed)
 
   def _createAlgorithmInterceptor(self, interceptor, image, pos):
@@ -165,6 +170,7 @@ def _withBlur(self, reverseArgs, B):
   def _inference(
     self, src, pos, 
     batchSize, reverseArgs, initialValues, encoderParams,
+    GT=None
   ):
     B = tf.shape(src)[0]
     N = tf.shape(pos)[0]
@@ -206,8 +212,14 @@ def getChunk(ind, sz):
       residual = self._withResidual(src, points=posCB)
       residual = tf.reshape(residual, (-1, 3))
       tf.assert_equal(tf.shape(residual), (flatB, 3))
+
+      reverseArgsNew = reverseArgs
+      if GT is not None:
+        # add ground truth values to copy of reverseArgs
+        reverseArgsNew = {**reverseArgs, 'GT': tf.reshape(GT[:, ind:ind+sz], (flatB, 3))}
+        pass
       return dict(
-        latents=latents, pos=posC, reverseArgs=reverseArgs, value=value,
+        latents=latents, pos=posC, reverseArgs=reverseArgsNew, value=value,
         residual=residual
       )
 
@@ -226,6 +238,7 @@ def call(self,
     batchSize=None, # renderers batch size
     initialValues=None, # initial values for the restoration process
     reverseArgs=None,
+    GT=None, # ground truth values for debugging purposes
   ):
     src = ensure4d(src)
     B = tf.shape(src)[0]
@@ -258,7 +271,8 @@ def call(self,
       batchSize=batchSize,
       reverseArgs=reverseArgs,
       encoderParams=encoderParams,
-      initialValues=initialValues
+      initialValues=initialValues,
+      GT=GT
     )
     probes = tf.reshape(probes, sampleShape)
     return probes