added normalisation

removed useless branch
Improved training script

NN/Utils.py

@@ -232,6 +232,7 @@ def __init__(self, mlp=None, **kwargs):
     super().__init__(**kwargs)
     if mlp is None: mlp = lambda x: x
     self._mlp = mlp
+    self._norm = L.LayerNormalization()
     return
 
   def build(self, input_shapes):
@@ -243,6 +244,7 @@ def build(self, input_shapes):
   def call(self, x):
     assert isinstance(x, list), "expected list of inputs"
     xhat = tf.concat(x, axis=-1)
+    xhat = self._norm(xhat)
     xhat = self._mlp(xhat)
     xhat = self._lastDense(xhat)
     x0 = x[0]

NN/networks.py

@@ -185,36 +185,7 @@ def Face2LatentModel(
   IP = lambda x: IntermediatePredictor()(x) # own IntermediatePredictor for each output
   res['intermediate'] = {k: IP(x) for k, x in intermediate.items()}
   res['result'] = IP(res['latent'])
-  ###################################
-  # TODO: figure out is this helpful or not
-  # branch for global coordinates transformation
-  # predict shift, rotation, scale
-  emb = L.Concatenate(-1)([userIdEmb, placeIdEmb, screenIdEmb])
-  emb = sMLP(sizes=[64, 64, 64, 64, 32], activation='relu')(emb[:, 0])
-  shift = L.Dense(2, name='GlobalShift')(emb)[:, None]
-  rotation = L.Dense(1, name='GlobalRotation', activation='sigmoid')(emb)[:, None] * np.pi
-  scale = L.Dense(2, name='GlobalScale')(emb)[:, None]
-
-  shifted = res['result'] + shift - 0.5 # [0.5, 0.5] -> [0, 0]
-  # Rotation matrix components
-  cos_rotation = L.Lambda(lambda x: tf.cos(x))(rotation)
-  sin_rotation = L.Lambda(lambda x: tf.sin(x))(rotation)
-  rotation_matrix = L.Lambda(lambda x: tf.stack([x[0], x[1]], axis=-1))([cos_rotation, sin_rotation])
-
-  # Apply rotation
-  rotated = L.Lambda(
-    lambda x: tf.einsum('isj,iomj->isj', x[0], x[1])
-  )([shifted, rotation_matrix]) + 0.5 # [0, 0] -> [0.5, 0.5] back
-
-  # Apply scale
-  scaled = rotated * scale
-  def clipWithGradient(x):
-    res = tf.clip_by_value(x, 0.0, 1.0)
-    return x + tf.stop_gradient(res - x)
 
-  res['result'] = L.Lambda(clipWithGradient)(scaled)
-  ###################################
-
   main = tf.keras.Model(inputs=inputs, outputs=res)
   return {
     'main': main,

scripts/train.py

@@ -239,31 +239,52 @@ def main(args):
   model = trainer(**model)
   model._model.summary()
 
-  if args.average:
-    averageModels(folder, model)
   # find folders with the name "/test-*/"
   evalDatasets = [
     CTestLoader(nm)
     for nm in glob.glob(os.path.join(folder, 'test-main', 'test-*/'))
   ]
   eval = evaluator(evalDatasets, model, folder, args)
-  bestLoss = eval()
+  bestLoss = eval() # evaluate loaded model
   bestEpoch = 0
+  # wrapper for the evaluation function. It saves the model if it is better
+  def evalWrapper(eval):
+    def f(epoch, onlyImproved=False):
+      nonlocal bestLoss, bestEpoch
+      newLoss = eval(onlyImproved=onlyImproved)
+      if newLoss < bestLoss:
+        print('Improved %.5f => %.5f' % (bestLoss, newLoss))
+        bestLoss = newLoss
+        bestEpoch = epoch
+        model.save(folder, postfix='best')
+      return
+    return f
+
+  eval = evalWrapper(eval)
+
+  def performRandomSearch(epoch=0):
+    nonlocal bestLoss, bestEpoch
+    averageModels(folder, model, noiseStd=0.0)
+    eval(epoch=epoch, onlyImproved=True) # evaluate the averaged model
+    for _ in range(args.restarts):
+      # and add some noise
+      averageModels(folder, model, noiseStd=args.noise)
+      # re-evaluate the model with the new weights
+      eval(epoch=epoch, onlyImproved=True)
+      continue
+    return
+
+  if args.average:
+    performRandomSearch()
+
   trainStep = _modelTrainingLoop(model, trainDataset)
   for epoch in range(args.epochs):
     trainStep(
       desc='Epoch %.*d / %d' % (len(str(args.epochs)), epoch, args.epochs),
       sampleParams=getSampleParams(epoch)
     )
     model.save(folder, postfix='latest')
-
-    testLoss = eval()
-    if testLoss < bestLoss:
-      print('Improved %.5f => %.5f' % (bestLoss, testLoss))
-      bestLoss = testLoss
-      bestEpoch = epoch
-      model.save(folder, postfix='best')
-      continue
+    eval(epoch)
 
     print('Passed %d epochs since the last improvement (best: %.5f)' % (epoch - bestEpoch, bestLoss))
     if args.patience <= (epoch - bestEpoch):
@@ -272,19 +293,7 @@ def main(args):
         break
       if 'reset' == args.on_patience:
         print('Resetting the model to the average of the best models')
-        bestEpoch = epoch # reset the best epoch
-        for _ in range(args.restarts):
-          # and add some noise
-          averageModels(folder, model, noiseStd=args.noise)
-          # re-evaluate the model with the new weights
-          testLoss = eval(onlyImproved=True)
-          if testLoss < bestLoss:
-            print('Improved %.5f => %.5f' % (bestLoss, testLoss))
-            bestLoss = testLoss
-            bestEpoch = epoch
-            model.save(folder, postfix='best')
-            continue
-          continue
+        performRandomSearch(epoch=epoch)
     continue
   return