Better support for converted Keras models (#12)

* support activation layers (with dense only) and ignore inputlayer

* clean up

* test new support

* bump version

Author: sshane

konverter/__init__.py

@@ -1,4 +1,4 @@
-from konverter.utils.model_attributes import Activations, Layers, watermark
+from konverter.utils.model_attributes import Activations, Layers, LAYERS_IGNORED, watermark
 from konverter.utils.konverter_support import KonverterSupport
 from konverter.utils.general import success, error, info, warning, COLORS
 import numpy as np
@@ -45,7 +45,7 @@ def start(self):
     self.get_layers()
     if self.verbose:
       self.print_model_architecture()
-    self.remove_unused_layers()
+    self.remove_ignored_layers()
     self.parse_output_file()
     self.build_konverted_model()
 
@@ -72,7 +72,7 @@ def build_konverted_model(self):
       if layer.name == Layers.Dense.name:
         model_line = f'l{idx} = {layer.string.format(prev_output, idx, idx)}'
         model_builder['model'].append(model_line)
-        if layer.info.has_activation:
+        if layer.info.has_activation and layer.info.activation.name != Activations.Linear.name:
           if layer.info.activation.needs_function:
             lyr_w_act = f'l{idx} = {layer.info.activation.alias.lower()}(l{idx})'
           else:  # eg. tanh or relu
@@ -165,8 +165,8 @@ def save_model(self, model_builder):
     with open(f'{self.output_file}.py', 'w') as f:
       f.write(output.replace('\t', self.indent))
 
-  def remove_unused_layers(self):
-    self.layers = [layer for layer in self.layers if layer.name not in support.unused_layers]
+  def remove_ignored_layers(self):
+    self.layers = [layer for layer in self.layers if layer.name not in LAYERS_IGNORED]
 
   def parse_output_file(self):
     if self.output_file is None:  # user hasn't supplied output file path, use input file name in same dir
@@ -186,7 +186,8 @@ def parse_output_file(self):
   def print_model_architecture(self):
     success('\nSuccessfully got model architecture! 😄\n')
     info('Layers:')
-    to_print = [[COLORS.BASE.format(74) + f'name: {layer.alias}' + COLORS.ENDC] for layer in self.layers]
+    ignored_txt = {True: ' (ignored)', False: ''}
+    to_print = [[COLORS.BASE.format(74) + f'name: {layer.alias}{ignored_txt[layer.info.is_ignored]}' + COLORS.ENDC] for layer in self.layers]
     max_len = 0
     indentation = '  '
     for idx, layer in enumerate(self.layers):
@@ -205,8 +206,12 @@ def print_model_architecture(self):
     print(COLORS.ENDC, end='')
 
   def get_layers(self):
-    for layer in self.model.layers:
-      layer = support.get_layer_info(layer)
+    for idx, layer in enumerate(self.model.layers):
+      next_layer = None
+      if idx < len(self.model.layers) - 1:
+        next_layer = self.model.layers[idx + 1]
+
+      layer = support.get_layer_info(layer, next_layer)
       if layer.info.supported:
         self.layers.append(layer)
       else:

konverter/__main__.py

@@ -3,7 +3,7 @@
 import konverter
 from konverter.utils.general import success, info, warning, error, COLORS, color_logo, blue_grad
 
-KONVERTER_VERSION = "v0.2.4.1"  # fixme: unify this
+KONVERTER_VERSION = "v0.2.5"  # fixme: unify this
 KONVERTER_LOGO_COLORED = color_logo(KONVERTER_VERSION)
 
 

konverter/utils/konverter_support.py

@@ -1,4 +1,5 @@
-from konverter.utils.model_attributes import BaseLayerInfo, BaseModelInfo, Models, Activations, Layers
+from konverter.utils.model_attributes import BaseLayerInfo, BaseModelInfo, Models, Activations, Layers, \
+                                             LAYERS_NO_ACTIVATION, LAYERS_IGNORED, LAYERS_RECURRENT
 import numpy as np
 
 
@@ -8,11 +9,6 @@ def __init__(self):
     self.layers = [getattr(Layers, i) for i in dir(Layers) if '_' not in i]
     self.activations = [getattr(Activations, i) for i in dir(Activations) if '_' not in i]
 
-    self.attrs_without_activations = [Layers.Dropout.name, Activations.Linear.name, Layers.BatchNormalization.name]
-    self.unused_layers = [Layers.Dropout.name]
-    self.recurrent_layers = [Layers.SimpleRNN.name, Layers.GRU.name]
-    self.ignored_layers = [Layers.Dropout.name]
-
   def get_class_from_name(self, name, search_in):
     """
     :param name: A name of an attribute, ex. keras.layers.Dense, keras.activations.relu
@@ -73,63 +69,78 @@ def get_model_info(self, model):
 
     return model_class
 
-  def get_layer_info(self, layer):
+  @staticmethod
+  def _get_layer_name(layer):
     name = getattr(layer, '_keras_api_names_v1')
     if not len(name):
       name = getattr(layer, '_keras_api_names')
+    return name
+
+  def _get_layer_activation(self, layer):
+    if hasattr(layer.activation, '_keras_api_names'):
+      activation = getattr(layer.activation, '_keras_api_names')
+    else:  # fixme: TF 2.3 is missing _keras_api_names
+      activation = 'keras.activations.' + getattr(layer.activation, '__name__')
+      activation = (activation,)
+
+    if len(activation) == 1:
+      return self.get_class_from_name(activation[0], 'activations')
+    else:
+      raise Exception('None or multiple activations?')
+
+  def get_layer_info(self, layer, next_layer):
+    # Identify layer
+    name = self._get_layer_name(layer)
     layer_class = self.get_class_from_name(name[0], 'layers')  # assume only one name
     layer_class.info = BaseLayerInfo()
     if not layer_class:
       layer_class = Layers.Unsupported()  # add activation below to raise exception with
       layer_class.name = name
 
-    layer_class.info.is_ignored = layer_class.name in self.ignored_layers
-
-    is_linear = False
-    if layer_class.name not in self.attrs_without_activations:
-      if hasattr(layer.activation, '_keras_api_names'):
-        activation = getattr(layer.activation, '_keras_api_names')
-      else:  # fixme: TF 2.3 is missing _keras_api_names
-        activation = 'keras.activations.' + getattr(layer.activation, '__name__')
-        activation = (activation,)  # fixme: expects this as a tuple
-
-      if len(activation) == 1:
-        layer_class.info.activation = self.get_class_from_name(activation[0], 'activations')
-        if layer_class.info.activation.name not in self.attrs_without_activations:
-          layer_class.info.has_activation = True
-        else:
-          is_linear = True
-      else:
-        raise Exception('None or multiple activations?')
-
-    if layer_class.info.has_activation:
-      if layer_class.info.activation.name == 'keras.layers.LeakyReLU':  # set alpha
+    layer_class.info.is_ignored = layer_class.name in LAYERS_IGNORED
+
+    # Handle layer activation
+    if layer_class.name not in LAYERS_NO_ACTIVATION:
+      layer_class.info.activation = self._get_layer_activation(layer)
+      layer_class.info.has_activation = True
+
+      # Note: special case for when activation is a separate layer after dense
+      if layer_class.name == Layers.Dense.name and layer_class.info.activation.name == Activations.Linear.name:
+        if next_layer is not None and self._get_layer_name(next_layer)[0] == Layers.Activation.name:
+          layer_class.info.activation = self._get_layer_activation(next_layer)
+
+    # Check if layer is supported given ignored status and activation
+    if layer_class.info.has_activation and not layer_class.info.is_ignored:
+      if layer_class.info.activation.name == Activations.LeakyReLU.name:  # set alpha
         layer_class.info.activation.alpha = round(float(layer.activation.alpha), 5)
 
       # check layer activation against this layer's supported activations
       if layer_class.info.activation.name in self.attr_map(layer_class.supported_activations, 'name'):
         layer_class.info.supported = True
-    elif layer_class.info.is_ignored or is_linear:  # skip activation check if layer has no activation (eg. dropout or linear)
+
+    elif layer_class.info.is_ignored:
       layer_class.info.supported = True
-    elif layer_class.name in self.attrs_without_activations:
+
+    elif layer_class.name in LAYERS_NO_ACTIVATION:  # skip activation check if layer has no activation (eg. dropout)
       layer_class.info.supported = True
 
     # if not layer_class.info.supported or (not is_linear and not layer_class.info.has_activation):
     #   return layer_class
     if not layer_class.info.supported:
       return layer_class
 
+    # Parse weights and biases from layer if available
     try:
       wb = layer.get_weights()
       if len(wb) == 0:
         return layer_class
     except:
       return layer_class
 
-    if len(wb) == 2:
+    if len(wb) == 2:  # Dense
       layer_class.info.weights = np.array(wb[0])
       layer_class.info.biases = np.array(wb[1])
-    elif len(wb) == 3 and layer_class.name in self.recurrent_layers:
+    elif len(wb) == 3 and layer_class.name in LAYERS_RECURRENT:
       layer_class.info.weights = np.array(wb[:2])  # input and recurrent weights
       layer_class.info.biases = np.array(wb[-1])
       layer_class.info.returns_sequences = layer.return_sequences

konverter/utils/model_attributes.py

@@ -90,6 +90,14 @@ class Dropout(_BaseLayer):
     name = 'keras.layers.Dropout'
     alias = 'dropout'
 
+  class InputLayer(_BaseLayer):
+    name = 'keras.layers.InputLayer'
+    alias = 'InputLayer'
+
+  class Activation(_BaseLayer):
+    name = 'keras.layers.Activation'
+    alias = 'Activation'
+
   class BatchNormalization(_BaseLayer):
     name = 'keras.layers.BatchNormalization'
     alias = 'batch_norm'
@@ -125,6 +133,12 @@ class Unsupported(_BaseLayer):  # propogated with layer info and returned to Kon
     pass
 
 
+# LAYERS_NO_ACTIVATION = [Layers.Dropout.name, Layers.InputLayer.name, Activations.Linear.name, Layers.BatchNormalization.name]
+LAYERS_NO_ACTIVATION = [Layers.Dropout.name, Layers.InputLayer.name, Layers.BatchNormalization.name]
+LAYERS_IGNORED = [Layers.Dropout.name, Layers.InputLayer.name, Layers.Activation.name]
+LAYERS_RECURRENT = [Layers.SimpleRNN.name, Layers.GRU.name]
+
+
 class BaseModelInfo:
   supported = False
   input_shape = None  # this will need to be moved if we support functional models

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "keras-konverter"
-version = "0.2.4.1"
+version = "0.2.5"
 description = "A tool to convert simple Keras models to pure Python + NumPy"
 readme = "README.md"
 repository = "https://github.com/ShaneSmiskol/Konverter"

tests/build_test_models.py

@@ -1,5 +1,5 @@
 import numpy as np
-from tensorflow.keras.layers import Dense, SimpleRNN, GRU, BatchNormalization
+from tensorflow.keras.layers import Dense, SimpleRNN, GRU, BatchNormalization, InputLayer, Activation
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.backend import clear_session
 
@@ -14,9 +14,12 @@ def create_model(model_type):
     y_train = (np.mean(x_train, axis=1) ** 2) / 2  # half of squared mean of sample
 
     model = Sequential()
-    model.add(Dense(128, activation='relu', input_shape=x_train.shape[1:]))
+    model.add(InputLayer(input_shape=x_train.shape[1:]))
+    model.add(Dense(128))
+    model.add(Activation(activation='relu'))
     model.add(BatchNormalization())
-    model.add(Dense(64, activation='tanh'))
+    model.add(Dense(64))
+    model.add(Activation(activation='tanh'))
     model.add(BatchNormalization())
     model.add(Dense(32, activation='relu'))
     model.add(BatchNormalization())