Support tf.nn.relu6

keras2onnx/_builtin.py

@@ -1723,6 +1723,16 @@ def convert_tf_read_variable_op(scope, operator, container):
  name=operator.full_name)
 
 
+@converter_func(TYPES.Relu6)
+def convert_tf_relu6(scope, operator, container):
+ oopb = OnnxOperatorBuilder(container, scope)
+ oopb.apply_op_with_output("apply_relu6",
+ operator.input_full_names,
+ operator.output_full_names,
+ name=operator.full_name + '_clip',
+ dtype=operator.raw_operator.input.dtype.as_numpy_dtype)
+
+
 @converter_func(TYPES.Slice)
 def convert_tf_slice(scope, operator, container):
  oopb = OnnxOperatorBuilder(container, scope)

keras2onnx/_consts.py

@@ -62,6 +62,7 @@ class TYPES:
  Range = 'Range'
  ReadVariableOp = 'ReadVariableOp'
  RealDiv = 'RealDiv'
+ Relu6 = 'Relu6'
  Reshape = 'Reshape'
  ResizeBilinear = 'ResizeBilinear'
  ResizeNearestNeighbor = 'ResizeNearestNeighbor'

keras2onnx/ke2onnx/activation.py

@@ -5,8 +5,8 @@
 ###############################################################################
 import tensorflow as tf
 from ..proto import keras, is_tf_keras
-from ..common.onnx_ops import apply_elu, apply_hard_sigmoid, apply_relu, apply_sigmoid, apply_tanh, \
- apply_softmax, apply_identity, apply_selu, apply_clip, apply_mul
+from ..common.onnx_ops import apply_elu, apply_hard_sigmoid, apply_relu, apply_relu6, apply_sigmoid, apply_tanh, \
+ apply_softmax, apply_identity, apply_selu, apply_mul
 
 activation_get = keras.activations.get
 
@@ -30,6 +30,7 @@
  tf.nn.sigmoid: apply_sigmoid,
  tf.nn.softmax: apply_softmax,
  tf.nn.relu: apply_relu,
+ tf.nn.relu6: apply_relu6,
  tf.nn.elu: apply_elu,
  tf.nn.tanh: apply_tanh}
 
@@ -56,9 +57,8 @@ def convert_keras_activation(scope, operator, container):
  apply_selu(scope, input_name, output_name, container, alpha=1.673263, gamma=1.050701)
  elif activation in [relu6] or activation.__name__ == 'relu6':
  # relu6(x) = min(relu(x), 6)
- apply_relu(scope, input_name, output_name + "_relu6", container)
- apply_clip(scope, output_name + "_relu6", output_name, container,
- min=0, max=6)
+ apply_relu6(scope, input_name, output_name, container,
+ dtype=operator.raw_operator.input.dtype.as_numpy_dtype)
  elif activation.__name__ in ['swish']:
  apply_sigmoid(scope, input_name, output_name + '_sig', container)
  apply_mul(scope, [input_name, output_name + '_sig'], output_name, container)

keras2onnx/ke2onnx/common.py

@@ -4,8 +4,26 @@
 # license information.
 ###############################################################################
 
+from ..proto import keras
+from ..proto.tfcompat import tensorflow as tf
+from ..common.onnx_ops import apply_relu6, apply_softmax
+from .activation import activation_map
+activation_get = keras.activations.get
+
 
 def get_permutation_config(n_dims):
  input_perm_axes = [0, n_dims + 1] + list(range(1, n_dims + 1))
  output_perm_axes = [0] + list(range(2, n_dims + 2)) + [1]
  return input_perm_axes, output_perm_axes
+
+
+def activation_process(scope, operator, container, biased_tensor_name):
+ # Create an activation function node and apply activation function to the intermediate tensor
+ apply_activation_function = activation_map[operator.raw_operator.activation]
+ if operator.raw_operator.activation in [activation_get('softmax'), keras.activations.softmax]:
+ apply_softmax(scope, biased_tensor_name, operator.outputs[0].full_name, container, axis=-1)
+ elif operator.raw_operator.activation in [tf.nn.relu6]:
+ apply_relu6(scope, biased_tensor_name, operator.outputs[0].full_name, container,
+ dtype=operator.raw_operator.input.dtype.as_numpy_dtype)
+ else:
+ apply_activation_function(scope, biased_tensor_name, operator.outputs[0].full_name, container)

keras2onnx/ke2onnx/conv.py

@@ -4,11 +4,11 @@
 # license information.
 ###############################################################################
 import numpy
-from .activation import activation_map
+from .common import activation_process
 from ..proto import keras
 from ..proto import onnx_proto
 from ..common.utils import count_dynamic_dim
-from ..common.onnx_ops import (apply_identity, apply_pad, apply_softmax,
+from ..common.onnx_ops import (apply_identity, apply_pad,
  apply_transpose, apply_mul, apply_sigmoid)
 
 activation_get = keras.activations.get
@@ -202,11 +202,7 @@ def convert_keras_conv_core(scope, operator, container, is_transpose, n_dims, in
  apply_mul(scope, [transpose_output_name, transpose_output_name + '_sig'], operator.outputs[0].full_name,
  container)
  else:
- apply_activation_function = activation_map[op.activation]
- if op.activation in [activation_get('softmax'), keras.activations.softmax]:
- apply_softmax(scope, transpose_output_name, operator.outputs[0].full_name, container, axis=-1)
- else:
- apply_activation_function(scope, transpose_output_name, operator.outputs[0].full_name, container)
+ activation_process(scope, operator, container, transpose_output_name)
 
 
 def get_converter_config(dims, is_conv_transpose):

keras2onnx/ke2onnx/dense.py

@@ -4,9 +4,9 @@
 # license information.
 ###############################################################################
 import numpy as np
+from .common import activation_process
 from ..proto import onnx_proto, keras
-from ..common.onnx_ops import apply_softmax, apply_add, OnnxOperatorBuilder
-from .activation import activation_map
+from ..common.onnx_ops import apply_add, OnnxOperatorBuilder
 activation_get = keras.activations.get
 
 
@@ -40,9 +40,4 @@ def convert_keras_dense(scope, operator, container):
  apply_add(scope, transformed_tensor_name + [bias_name], biased_tensor_name, container,
  axis=-1, broadcast=1)
 
- # Create an activation function node and apply activation function to the intermediate tensor
- apply_activation_function = activation_map[operator.raw_operator.activation]
- if operator.raw_operator.activation in [activation_get('softmax'), keras.activations.softmax]:
- apply_softmax(scope, biased_tensor_name, operator.outputs[0].full_name, container, axis=-1)
- else:
- apply_activation_function(scope, biased_tensor_name, operator.outputs[0].full_name, container)
+ activation_process(scope, operator, container, biased_tensor_name)

tests/test_layers.py

@@ -1374,7 +1374,7 @@ def test_Softmax(advanced_activation_runner):
 
 
 def test_tf_nn_activation(runner):
- for activation in [tf.nn.relu, 'relu']:
+ for activation in [tf.nn.relu, 'relu', tf.nn.relu6]:
  model = keras.Sequential([
  Dense(64, activation=activation, input_shape=[10]),
  Dense(64, activation=activation),