Merge pull request #82 from Vent1narc/master

onnx:ELU support

README.md

@@ -8,7 +8,7 @@
 [![Readthedocs](https://img.shields.io/readthedocs/pytorch2keras.svg)](https://pytorch2keras.readthedocs.io/en/latest/)
 
 
-PyTorch to Keras model converter. 
+PyTorch to Keras model converter.
 
 ## Installation
 
@@ -207,6 +207,7 @@ Options:
 ## Supported layers
 
 * Activations:
+    + ELU
     + ReLU
     + LeakyReLU
     + SELU
@@ -319,4 +320,4 @@ Options:
 Look at the `tests` directory.
 
 ## License
-This software is covered by MIT License.
+This software is covered by MIT License.

pytorch2keras/activation_layers.py

@@ -6,6 +6,32 @@
 from .common import random_string
 
 
+def convert_elu(params, w_name, scope_name, inputs, layers, weights, names):
+    """
+    Convert elu layer.
+
+    Args:
+        params: dictionary with layer parameters
+        w_name: name prefix in state_dict
+        scope_name: pytorch scope name
+        inputs: pytorch node inputs
+        layers: dictionary with keras tensors
+        weights: pytorch state_dict
+        names: use short names for keras layers
+    """
+    print('Converting elu ...')
+
+    if names == 'short':
+        tf_name = 'ELU' + random_string(4)
+    elif names == 'keep':
+        tf_name = w_name
+    else:
+        tf_name = w_name + str(random.random())
+
+    elu = keras.layers.Activation('elu', name=tf_name)
+    layers[scope_name] = elu(layers[inputs[0]])
+
+
 def convert_relu(params, w_name, scope_name, inputs, layers, weights, names):
     """
     Convert relu layer.

pytorch2keras/convolution_layers.py

@@ -133,7 +133,7 @@ def convolve_lambda(i, k):
                     return tf.nn.conv2d(i, k, strides=[1, stride_y, stride_x, 1], padding='VALID')
 
                 input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)
-                weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=W.transpose(0, 1, 2, 3))
+                weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=np.array(W, dtype=np.float32).transpose(0, 1, 2, 3))
                 output_groups = [convolve_lambda(i, k) for i, k in zip(input_groups, weight_groups)]
 
                 layer = tf.concat(axis=3, values=output_groups)

pytorch2keras/layers.py

@@ -11,7 +11,7 @@
 from .elementwise_layers import convert_elementwise_add, convert_elementwise_mul, \
     convert_elementwise_div, convert_elementwise_sub
 from .activation_layers import convert_relu, convert_lrelu, convert_selu, \
-    convert_softmax, convert_sigmoid, convert_tanh, convert_hardtanh
+    convert_softmax, convert_sigmoid, convert_tanh, convert_hardtanh, convert_elu
 from .pooling_layers import convert_avgpool, convert_maxpool, convert_maxpool3, \
     convert_adaptive_avg_pool2d, convert_adaptive_max_pool2d
 from .normalization_layers import convert_batchnorm, convert_instancenorm, convert_dropout
@@ -45,6 +45,7 @@
     'onnx::Sub': convert_elementwise_sub,
     'onnx::Sum': convert_sum,
     'onnx::Concat': convert_concat,
+    'onnx::Elu': convert_elu,
     'onnx::Relu': convert_relu,
     'onnx::LeakyRelu': convert_lrelu,
     'onnx::Sigmoid': convert_sigmoid,