Removed tensorflow specific code in presets (#59)

* Add generic layer specification for using in presets

* Modify presets to use the generic scheme

rl_coach/architectures/tensorflow_components/embedders/embedder.py

@@ -20,8 +20,7 @@ import copy
 import numpy as np
 import tensorflow as tf
 
-from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense, \
-    BatchnormActivationDropout
+from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense
 from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
 
 from rl_coach.core_types import InputEmbedding
@@ -62,7 +61,9 @@ class InputEmbedder(object):
         if isinstance(self.scheme, EmbedderScheme):
             self.layers_params = copy.copy(self.schemes[self.scheme])
         else:
-            self.layers_params = copy.copy(self.scheme)
+            # if scheme is specified directly, convert to TF layer if it's not a callable object
+            # NOTE: if layer object is callable, it must return a TF tensor when invoked
+            self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)]
 
         # we allow adding batchnorm, dropout or activation functions after each layer.
         # The motivation is to simplify the transition between a network with batchnorm and a network without

rl_coach/architectures/tensorflow_components/general_network.py

@@ -24,6 +24,7 @@ from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.architectures.head_parameters import HeadParameters
 from rl_coach.architectures.middleware_parameters import MiddlewareParameters
 from rl_coach.architectures.tensorflow_components.architecture import TensorFlowArchitecture
+from rl_coach.architectures.tensorflow_components import utils
 from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType
 from rl_coach.core_types import PredictionType
 from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace
@@ -99,27 +100,6 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
 
         return ret_dict
 
-    @staticmethod
-    def get_activation_function(activation_function_string: str):
-        """
-        Map the activation function from a string to the tensorflow framework equivalent
-        :param activation_function_string: the type of the activation function
-        :return: the tensorflow activation function
-        """
-        activation_functions = {
-            'relu': tf.nn.relu,
-            'tanh': tf.nn.tanh,
-            'sigmoid': tf.nn.sigmoid,
-            'elu': tf.nn.elu,
-            'selu': tf.nn.selu,
-            'leaky_relu': tf.nn.leaky_relu,
-            'none': None
-        }
-        assert activation_function_string in activation_functions.keys(), \
-            "Activation function must be one of the following {}. instead it was: {}"\
-                .format(activation_functions.keys(), activation_function_string)
-        return activation_functions[activation_function_string]
-
     def get_input_embedder(self, input_name: str, embedder_params: InputEmbedderParameters):
         """
         Given an input embedder parameters class, creates the input embedder and returns it
@@ -144,7 +124,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
 
         embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type]
         embedder_params_copy = copy.copy(embedder_params)
-        embedder_params_copy.activation_function = self.get_activation_function(embedder_params.activation_function)
+        embedder_params_copy.activation_function = utils.get_activation_function(embedder_params.activation_function)
         embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type]
         embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type]
         embedder_params_copy.name = input_name
@@ -162,7 +142,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
         mod_name = middleware_params.parameterized_class_name
         middleware_path = 'rl_coach.architectures.tensorflow_components.middlewares:' + mod_name
         middleware_params_copy = copy.copy(middleware_params)
-        middleware_params_copy.activation_function = self.get_activation_function(middleware_params.activation_function)
+        middleware_params_copy.activation_function = utils.get_activation_function(middleware_params.activation_function)
         module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path)
         return module
 
@@ -176,7 +156,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
         mod_name = head_params.parameterized_class_name
         head_path = 'rl_coach.architectures.tensorflow_components.heads:' + mod_name
         head_params_copy = copy.copy(head_params)
-        head_params_copy.activation_function = self.get_activation_function(head_params_copy.activation_function)
+        head_params_copy.activation_function = utils.get_activation_function(head_params_copy.activation_function)
         return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={
             'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name,
             'head_idx': head_idx, 'is_local': self.network_is_local})

rl_coach/architectures/tensorflow_components/layers.py

@@ -1,9 +1,11 @@
 import math
-from typing import List, Union
+from types import FunctionType
+from typing import Any
 
 import tensorflow as tf
 
-from rl_coach.utils import force_list
+from rl_coach.architectures import layers
+from rl_coach.architectures.tensorflow_components import utils
 
 
 def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dropout, dropout_rate, is_training, name):
@@ -17,6 +19,8 @@ def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dr
 
     # activation
     if activation_function:
+        if isinstance(activation_function, str):
+            activation_function = utils.get_activation_function(activation_function)
         layers.append(
             activation_function(layers[-1], name="{}_activation".format(name))
         )
@@ -33,11 +37,35 @@ def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dr
     return layers
 
 
-class Conv2d(object):
+# define global dictionary for storing layer type to layer implementation mapping
+tf_layer_dict = dict()
+
+
+def reg_to_tf(layer_type) -> FunctionType:
+    """ function decorator that registers layer implementation
+    :return: decorated function
+    """
+    def reg_impl_decorator(func):
+        assert layer_type not in tf_layer_dict
+        tf_layer_dict[layer_type] = func
+        return func
+    return reg_impl_decorator
+
+
+def convert_layer(layer):
+    """
+    If layer is callable, return layer, otherwise convert to TF type
+    :param layer: layer to be converted
+    :return: converted layer if not callable, otherwise layer itself
+    """
+    if callable(layer):
+        return layer
+    return tf_layer_dict[type(layer)](layer)
+
+
+class Conv2d(layers.Conv2d):
     def __init__(self, num_filters: int, kernel_size: int, strides: int):
-        self.num_filters = num_filters
-        self.kernel_size = kernel_size
-        self.strides = strides
+        super(Conv2d, self).__init__(num_filters=num_filters, kernel_size=kernel_size, strides=strides)
 
     def __call__(self, input_layer, name: str=None, is_training=None):
         """
@@ -49,16 +77,19 @@ class Conv2d(object):
         return tf.layers.conv2d(input_layer, filters=self.num_filters, kernel_size=self.kernel_size,
                                 strides=self.strides, data_format='channels_last', name=name)
 
-    def __str__(self):
-        return "Convolution (num filters = {}, kernel size = {}, stride = {})"\
-            .format(self.num_filters, self.kernel_size, self.strides)
+    @staticmethod
+    @reg_to_tf(layers.Conv2d)
+    def to_tf(base: layers.Conv2d):
+        return Conv2d(
+            num_filters=base.num_filters,
+            kernel_size=base.kernel_size,
+            strides=base.strides)
 
 
-class BatchnormActivationDropout(object):
+class BatchnormActivationDropout(layers.BatchnormActivationDropout):
     def __init__(self, batchnorm: bool=False, activation_function=None, dropout_rate: float=0):
-        self.batchnorm = batchnorm
-        self.activation_function = activation_function
-        self.dropout_rate = dropout_rate
+        super(BatchnormActivationDropout, self).__init__(
+            batchnorm=batchnorm, activation_function=activation_function, dropout_rate=dropout_rate)
 
     def __call__(self, input_layer, name: str=None, is_training=None):
         """
@@ -72,20 +103,18 @@ class BatchnormActivationDropout(object):
                                             dropout=self.dropout_rate > 0, dropout_rate=self.dropout_rate,
                                             is_training=is_training, name=name)
 
-    def __str__(self):
-        result = []
-        if self.batchnorm:
-            result += ["Batch Normalization"]
-        if self.activation_function:
-            result += ["Activation (type = {})".format(self.activation_function.__name__)]
-        if self.dropout_rate > 0:
-            result += ["Dropout (rate = {})".format(self.dropout_rate)]
-        return "\n".join(result)
+    @staticmethod
+    @reg_to_tf(layers.BatchnormActivationDropout)
+    def to_tf(base: layers.BatchnormActivationDropout):
+        return BatchnormActivationDropout(
+            batchnorm=base.batchnorm,
+            activation_function=base.activation_function,
+            dropout_rate=base.dropout_rate)
 
 
-class Dense(object):
+class Dense(layers.Dense):
     def __init__(self, units: int):
-        self.units = units
+        super(Dense, self).__init__(units=units)
 
     def __call__(self, input_layer, name: str=None, kernel_initializer=None, activation=None, is_training=None):
         """
@@ -97,11 +126,13 @@ class Dense(object):
         return tf.layers.dense(input_layer, self.units, name=name, kernel_initializer=kernel_initializer,
                                activation=activation)
 
-    def __str__(self):
-        return "Dense (num outputs = {})".format(self.units)
+    @staticmethod
+    @reg_to_tf(layers.Dense)
+    def to_tf(base: layers.Dense):
+        return Dense(units=base.units)
 
 
-class NoisyNetDense(object):
+class NoisyNetDense(layers.NoisyNetDense):
     """
     A factorized Noisy Net layer
 
@@ -109,8 +140,7 @@ class NoisyNetDense(object):
     """
 
     def __init__(self, units: int):
-        self.units = units
-        self.sigma0 = 0.5
+        super(NoisyNetDense, self).__init__(units=units)
 
     def __call__(self, input_layer, name: str, kernel_initializer=None, activation=None, is_training=None):
         """
@@ -125,6 +155,16 @@ class NoisyNetDense(object):
         #      forward (either act or train, both for online and target networks).
         #      A3C, on the other hand, should sample noise only when policy changes (i.e. after every t_max steps)
 
+        def _f(values):
+            return tf.sqrt(tf.abs(values)) * tf.sign(values)
+
+        def _factorized_noise(inputs, outputs):
+            # TODO: use factorized noise only for compute intensive algos (e.g. DQN).
+            #      lighter algos (e.g. DQN) should not use it
+            noise1 = _f(tf.random_normal((inputs, 1)))
+            noise2 = _f(tf.random_normal((1, outputs)))
+            return tf.matmul(noise1, noise2)
+
         num_inputs = input_layer.get_shape()[-1].value
         num_outputs = self.units
 
@@ -145,23 +185,14 @@ class NoisyNetDense(object):
                                             initializer=kernel_stddev_initializer)
             bias_stddev = tf.get_variable('bias_stddev', shape=(num_outputs,),
                                           initializer=kernel_stddev_initializer)
-            bias_noise = self.f(tf.random_normal((num_outputs,)))
-            weight_noise = self.factorized_noise(num_inputs, num_outputs)
+            bias_noise = _f(tf.random_normal((num_outputs,)))
+            weight_noise = _factorized_noise(num_inputs, num_outputs)
 
         bias = bias_mean + bias_stddev * bias_noise
         weight = weight_mean + weight_stddev * weight_noise
         return activation(tf.matmul(input_layer, weight) + bias)
 
-    def factorized_noise(self, inputs, outputs):
-        # TODO: use factorized noise only for compute intensive algos (e.g. DQN).
-        #      lighter algos (e.g. DQN) should not use it
-        noise1 = self.f(tf.random_normal((inputs, 1)))
-        noise2 = self.f(tf.random_normal((1, outputs)))
-        return tf.matmul(noise1, noise2)
-
     @staticmethod
-    def f(values):
-        return tf.sqrt(tf.abs(values)) * tf.sign(values)
-
-    def __str__(self):
-        return "Noisy Dense (num outputs = {})".format(self.units)
+    @reg_to_tf(layers.NoisyNetDense)
+    def to_tf(base: layers.NoisyNetDense):
+        return NoisyNetDense(units=base.units)

rl_coach/architectures/tensorflow_components/middlewares/middleware.py

@@ -14,10 +14,11 @@
 # limitations under the License.
 #
 import copy
+from typing import Union
 
 import tensorflow as tf
 
-from rl_coach.architectures.tensorflow_components.layers import Dense, BatchnormActivationDropout
+from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense
 from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters
 from rl_coach.core_types import MiddlewareEmbedding
 
@@ -50,7 +51,9 @@ class Middleware(object):
         if isinstance(self.scheme, MiddlewareScheme):
             self.layers_params = copy.copy(self.schemes[self.scheme])
         else:
-            self.layers_params = copy.copy(self.scheme)
+            # if scheme is specified directly, convert to TF layer if it's not a callable object
+            # NOTE: if layer object is callable, it must return a TF tensor when invoked
+            self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)]
 
         # we allow adding batchnorm, dropout or activation functions after each layer.
         # The motivation is to simplify the transition between a network with batchnorm and a network without

rl_coach/architectures/tensorflow_components/utils.py

@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""
+Module containing utility functions
+"""
+import tensorflow as tf
+
+
+def get_activation_function(activation_function_string: str):
+    """
+    Map the activation function from a string to the tensorflow framework equivalent
+    :param activation_function_string: the type of the activation function
+    :return: the tensorflow activation function
+    """
+    activation_functions = {
+        'relu': tf.nn.relu,
+        'tanh': tf.nn.tanh,
+        'sigmoid': tf.nn.sigmoid,
+        'elu': tf.nn.elu,
+        'selu': tf.nn.selu,
+        'leaky_relu': tf.nn.leaky_relu,
+        'none': None
+    }
+    assert activation_function_string in activation_functions.keys(), \
+        "Activation function must be one of the following {}. instead it was: {}" \
+            .format(activation_functions.keys(), activation_function_string)
+    return activation_functions[activation_function_string]