diff --git a/nnvm/python/nnvm/frontend/keras.py b/nnvm/python/nnvm/frontend/keras.py
index eb9bf4d3720d26d237ae9d33a3e78b10a1dd5976..4ff60e3b3fc33acd3cb1baba4fbe3faaea347ccf 100644
--- a/nnvm/python/nnvm/frontend/keras.py
+++ b/nnvm/python/nnvm/frontend/keras.py
@@ -58,8 +58,10 @@ def _convert_activation(insym, keras_layer, _):
return _get_elu(insym, alpha)
elif act_type == 'selu':
# Alpha, Gamma values, obtained from https://arxiv.org/abs/1706.02515
- alpha = keras_layer.alpha if hasattr(keras_layer, "alpha") else 1.6732
- gamma = keras_layer.gamma if hasattr(keras_layer, "gamma") else 1.0507
+ alpha = keras_layer.alpha if hasattr(keras_layer, "alpha") \
+ else 1.6732632423543772848170429916717
+ gamma = keras_layer.gamma if hasattr(keras_layer, "gamma") \
+ else 1.0507009873554804934193349852946
return gamma * _get_elu(insym, alpha)
elif act_type == 'relu6':
return _sym.clip(insym, a_min=0, a_max=6)
@@ -155,8 +157,8 @@ def _convert_convolution(insym, keras_layer, symtab):
dilation = [keras_layer.dilation_rate[0], keras_layer.dilation_rate[1]]
else:
dilation = [keras_layer.dilation_rate, keras_layer.dilation_rate]
- kernel_h = (kernel_h - 1) * dilation[0] + 1
- kernel_w = (kernel_w - 1) * dilation[1] + 1
+ dilated_kernel_h = (kernel_h - 1) * dilation[0] + 1
+ dilated_kernel_w = (kernel_w - 1) * dilation[1] + 1
stride_h, stride_w = keras_layer.strides
params = {'weight': symtab.new_const(weight),
'kernel_size': [kernel_h, kernel_w],
@@ -178,8 +180,8 @@ def _convert_convolution(insym, keras_layer, symtab):
elif keras_layer.padding == 'same':
in_h = keras_layer.input_shape[1]
in_w = keras_layer.input_shape[2]
- pad_t, pad_b = _get_pad_pair(in_h, kernel_h, stride_h)
- pad_l, pad_r = _get_pad_pair(in_w, kernel_w, stride_w)
+ pad_t, pad_b = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
+ pad_l, pad_r = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
if pad_t == pad_b and pad_l == pad_r:
params['padding'] = (pad_t, pad_l)
else:
diff --git a/nnvm/tests/python/frontend/keras/test_forward.py b/nnvm/tests/python/frontend/keras/test_forward.py
index a8623b8a3976917636c1885f156e877c34de9d1c..92410ee8dbb3b6630680b9d8cef9005944a98827 100644
--- a/nnvm/tests/python/frontend/keras/test_forward.py
+++ b/nnvm/tests/python/frontend/keras/test_forward.py
@@ -73,10 +73,10 @@ def test_forward_elemwise_add():
keras_model = keras.models.Model(data, y)
verify_keras_frontend(keras_model)
+
def test_forward_dense():
- data = keras.layers.Input(shape=(32,32,3))
- x = keras.layers.MaxPooling2D(pool_size=(2,2))(data)
- x = keras.layers.Flatten()(x)
+ data = keras.layers.Input(shape=(32,32,1))
+ x = keras.layers.Flatten()(data)
x = keras.layers.Dropout(0.5)(x)
x = keras.layers.Dense(10, activation='relu', kernel_initializer='uniform')(x)
keras_model = keras.models.Model(data, x)
@@ -84,7 +84,7 @@ def test_forward_dense():
def test_forward_pool():
- data = keras.layers.Input(shape=(2,2,1))
+ data = keras.layers.Input(shape=(32,32,1))
# maxpool
x = keras.layers.MaxPooling2D((3, 3), strides=(1, 1), padding='same')(data)
keras_model = keras.models.Model(data, x)
@@ -95,25 +95,20 @@ def test_forward_pool():
verify_keras_frontend(keras_model)
-def test_forward_transpose_conv():
- data = keras.layers.Input(shape=(32,32,3))
- x = keras.layers.Conv2D(filters=10, kernel_size=(3,3), strides=(2,2), padding='same')(data)
- x = keras.layers.DepthwiseConv2D(kernel_size=(3,3), padding='same')(x)
- x = keras.layers.Conv2DTranspose(filters=64, kernel_size=(3,3), padding='valid')(x)
- x = keras.layers.GlobalMaxPooling2D()(x)
- keras_model = keras.models.Model(data, x)
- verify_keras_frontend(keras_model)
-
-
-def test_forward_separable_conv():
+def test_forward_conv():
data = keras.layers.Input(shape=(32,32,3))
- x = keras.layers.SeparableConv2D(filters=10, kernel_size=(3,3),
- padding='same', activation='relu')(data)
- x = keras.layers.BatchNormalization(scale=True, center=False,
- beta_initializer='uniform', gamma_initializer='uniform')(x)
- x = keras.layers.GlobalAveragePooling2D()(x)
- keras_model = keras.models.Model(data, x)
- verify_keras_frontend(keras_model)
+ conv_funcs = [keras.layers.Conv2D(filters=10, kernel_size=(3,3),
+ strides=(2,2), padding='same'),
+ keras.layers.Conv2D(filters=10, kernel_size=(3,3),
+ dilation_rate=(2,2), padding='same'),
+ keras.layers.DepthwiseConv2D(kernel_size=(3,3), padding='same'),
+ keras.layers.Conv2DTranspose(filters=10, kernel_size=(3,3), padding='valid'),
+ keras.layers.SeparableConv2D(filters=10, kernel_size=(3,3), padding='same')]
+ for conv_func in conv_funcs:
+ x = conv_func(data)
+ x = keras.layers.GlobalAveragePooling2D()(x)
+ keras_model = keras.models.Model(data, x)
+ verify_keras_frontend(keras_model)
def test_forward_upsample():
@@ -123,6 +118,7 @@ def test_forward_upsample():
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model)
+
def test_forward_reshape():
data = keras.layers.Input(shape=(32,32,3))
x = keras.layers.Reshape(target_shape=(32,32,3))(data)
@@ -168,6 +164,7 @@ def test_forward_mobilenet():
input_shape=(224,224,3), classes=1000)
verify_keras_frontend(keras_model)
+
def test_forward_activations():
data = keras.layers.Input(shape=(32,32,3))
weights = np.random.rand(1, 32, 32, 3)
@@ -187,10 +184,11 @@ def test_forward_activations():
keras.layers.Activation('linear')]
for act_func in act_funcs:
x = act_func(data)
- x = keras.layers.GlobalMaxPooling2D()(x)
+ x = keras.layers.GlobalAveragePooling2D()(x)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model)
+
def test_forward_multi_inputs():
data1 = keras.layers.Input(shape=(32,32,3))
data2 = keras.layers.Input(shape=(32,32,3))
@@ -239,8 +237,7 @@ if __name__ == '__main__':
test_forward_activations()
test_forward_dense()
test_forward_pool()
- test_forward_transpose_conv()
- test_forward_separable_conv()
+ test_forward_conv()
test_forward_upsample()
test_forward_reshape()
test_forward_crop()