Add Flatten layer to architectures + make flatten optional in embedde…

…rs (#483)

Flatten layer required for embedders that mix conv and dense
(Cherry picking from #478)

rl_coach/architectures/embedder_parameters.py

@@ -23,7 +23,7 @@
 class InputEmbedderParameters(NetworkComponentParameters):
     def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium,
                  batchnorm: bool=False, dropout_rate: float=0.0, name: str='embedder', input_rescaling=None,
-                 input_offset=None, input_clipping=None, dense_layer=None, is_training=False):
+                 input_offset=None, input_clipping=None, dense_layer=None, is_training=False, flatten=True):
         super().__init__(dense_layer=dense_layer)
         self.activation_function = activation_function
         self.scheme = scheme
@@ -40,6 +40,7 @@ def __init__(self, activation_function: str='relu', scheme: Union[List, Embedder
         self.input_clipping = input_clipping
         self.name = name
         self.is_training = is_training
+        self.flatten = flatten
 
     def path(self, emb_type):
         return 'rl_coach.architectures.tensorflow_components.embedders:' + MOD_NAMES[emb_type]

rl_coach/architectures/layers.py

@@ -76,3 +76,11 @@ def __init__(self, units: int):
 
     def __str__(self):
         return "Noisy Dense (num outputs = {})".format(self.units)
+
+
+class Flatten(object):
+    """
+    Base class for framework specific flatten layer (used to convert 3D convolution output to 1D dense input)
+    """
+    def __str__(self):
+        return "Flatten"

rl_coach/architectures/tensorflow_components/embedders/embedder.py

@@ -20,7 +20,8 @@
 import numpy as np
 import tensorflow as tf
 
-from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense
+from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense, \
+    Flatten
 from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
 
 from rl_coach.core_types import InputEmbedding
@@ -36,7 +37,7 @@ class InputEmbedder(object):
     def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
                  scheme: EmbedderScheme=None, batchnorm: bool=False, dropout_rate: float=0.0,
                  name: str= "embedder", input_rescaling=1.0, input_offset=0.0, input_clipping=None, dense_layer=Dense,
-                 is_training=False):
+                 is_training=False, flatten=True):
         self.name = name
         self.input_size = input_size
         self.activation_function = activation_function
@@ -55,6 +56,7 @@ def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
         if self.dense_layer is None:
             self.dense_layer = Dense
         self.is_training = is_training
+        self.flatten = flatten
 
         # layers order is conv -> batchnorm -> activation -> dropout
         if isinstance(self.scheme, EmbedderScheme):
@@ -116,7 +118,10 @@ def _build_module(self) -> None:
                              is_training=self.is_training)
             ))
 
-        self.output = tf.contrib.layers.flatten(self.layers[-1])
+        if self.flatten:
+            self.output = Flatten()(self.layers[-1])
+        else:
+            self.output = self.layers[-1]
 
     @property
     def input_size(self) -> List[int]:

rl_coach/architectures/tensorflow_components/embedders/image_embedder.py

@@ -34,9 +34,10 @@ class ImageEmbedder(InputEmbedder):
     def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
                  scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout_rate: float=0.0,
                  name: str= "embedder", input_rescaling: float=255.0, input_offset: float=0.0, input_clipping=None,
-                 dense_layer=Dense, is_training=False):
+                 dense_layer=Dense, is_training=False, flatten=True):
         super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling,
-                         input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training)
+                         input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training,
+                         flatten=flatten)
         self.return_type = InputImageEmbedding
         if len(input_size) != 3 and scheme != EmbedderScheme.Empty:
             raise ValueError("Image embedders expect the input size to have 3 dimensions. The given size is: {}"

rl_coach/architectures/tensorflow_components/embedders/tensor_embedder.py

@@ -41,9 +41,10 @@ class TensorEmbedder(InputEmbedder):
     def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
                  scheme: EmbedderScheme=None, batchnorm: bool=False, dropout_rate: float=0.0,
                  name: str= "embedder", input_rescaling: float=1.0, input_offset: float=0.0, input_clipping=None,
-                 dense_layer=Dense, is_training=False):
+                 dense_layer=Dense, is_training=False, flatten=True):
         super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling,
-                         input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training)
+                         input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training,
+                         flatten=flatten)
         self.return_type = InputTensorEmbedding
         assert scheme is not None, "Custom scheme (a list of callables) must be specified for TensorEmbedder"
 

rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py

@@ -33,10 +33,10 @@ class VectorEmbedder(InputEmbedder):
     def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
                  scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout_rate: float=0.0,
                  name: str= "embedder", input_rescaling: float=1.0, input_offset: float=0.0, input_clipping=None,
-                 dense_layer=Dense, is_training=False):
+                 dense_layer=Dense, is_training=False, flatten=True):
         super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name,
                          input_rescaling, input_offset, input_clipping, dense_layer=dense_layer,
-                         is_training=is_training)
+                         is_training=is_training, flatten=flatten)
 
         self.return_type = InputVectorEmbedding
         if len(self.input_size) != 1 and scheme != EmbedderScheme.Empty:

rl_coach/architectures/tensorflow_components/layers.py

@@ -264,3 +264,26 @@ def to_tf_instance(base: layers.NoisyNetDense):
     @reg_to_tf_class(layers.NoisyNetDense)
     def to_tf_class():
         return NoisyNetDense
+
+
+class Flatten(layers.Flatten):
+    def __init__(self):
+        super(Flatten, self).__init__()
+
+    def __call__(self, input_layer, **kwargs):
+        """
+        returns a tensorflow flatten layer
+        :param input_layer: previous layer
+        :return: flatten layer
+        """
+        return tf.contrib.layers.flatten(input_layer)
+
+    @staticmethod
+    @reg_to_tf_instance(layers.Flatten)
+    def to_tf_instance(base: layers.Flatten):
+        return Flatten()
+
+    @staticmethod
+    @reg_to_tf_class(layers.Flatten)
+    def to_tf_class():
+        return Flatten