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[Relay][Keras] Permute, Softmax support #3618

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Jul 25, 2019
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[Relay][Keras] Permute, Softmax support #3618

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16 changes: 13 additions & 3 deletions python/tvm/relay/frontend/keras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,9 @@ def _convert_activation(inexpr, keras_layer, _):

def _convert_advanced_activation(inexpr, keras_layer, etab):
act_type = type(keras_layer).__name__

if act_type == 'Softmax':
return _op.nn.softmax(inexpr, axis=1)
if act_type == 'ReLU':
if keras_layer.max_value:
return _op.clip(inexpr, a_min=0., a_max=float(keras_layer.max_value))
Expand Down Expand Up @@ -160,6 +163,8 @@ def _convert_merge(inexpr, keras_layer, _):
'Operator {} is not supported in frontend Keras.'.format(merge_type))
return ret

def _convert_permute(inexpr, keras_layer, _):
return _op.transpose(inexpr, axes=(0,) + keras_layer.dims)

def _convert_dense(inexpr, keras_layer, etab):
weightList = keras_layer.get_weights()
Expand Down Expand Up @@ -574,6 +579,7 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
_convert_map = {
'Dense' : _convert_dense,
'Activation' : _convert_activation,
'Softmax' : _convert_advanced_activation,
'ReLU' : _convert_advanced_activation,
'LeakyReLU' : _convert_advanced_activation,
'PReLU' : _convert_advanced_activation,
Expand Down Expand Up @@ -620,7 +626,7 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
'Average' : _convert_merge,
'Maximum' : _convert_merge,
# 'Dot' : _convert_merge,
# 'Permute' : _convert_permute,
'Permute' : _convert_permute,
# 'Embedding' : _convert_embedding,
# 'RepeatVector' : _convert_repeat_vector,

Expand All @@ -632,11 +638,15 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument


def _check_unsupported_layers(model):
missing_ops = set()
for layer in model.layers:
op_name = type(layer).__name__
if op_name not in _convert_map:
raise tvm.error.OpNotImplemented(
'Operator {} is not supported in frontend Keras.'.format(op_name))
missing_ops.add(op_name)

if missing_ops:
raise NotImplementedError( \
"The following operators are not implemented: {}".format(missing_ops))


def keras_op_to_relay(inexpr, keras_layer, outname, etab):
Expand Down
61 changes: 34 additions & 27 deletions tests/python/frontend/keras/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def to_channels_last(arr):


def test_forward_merge():
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
y = keras.layers.Conv2D(8, (3, 3), padding="same")(x)
z = keras.layers.Conv2D(8, (3, 3), padding="same")(y)
Expand All @@ -93,7 +93,7 @@ def test_forward_merge():


def test_forward_activations():
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
act_funcs = [keras.layers.Activation('softmax'),
keras.layers.Activation('softplus'),
keras.layers.Activation('relu'),
Expand All @@ -103,6 +103,7 @@ def test_forward_activations():
keras.layers.Activation('tanh'),
keras.layers.Activation('linear'),
keras.layers.Activation('selu'),
keras.layers.Softmax(),
keras.layers.ReLU(),
keras.layers.ReLU(max_value=6.),
keras.layers.LeakyReLU(alpha=0.3),
Expand All @@ -116,13 +117,18 @@ def test_forward_activations():


def test_forward_dense():
data = keras.layers.Input(shape=(32,32,1))
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)
verify_keras_frontend(keras_model)

def test_forward_permute():
data = keras.layers.Input(shape=(2, 3, 4))
x = keras.layers.Permute([2, 3, 1])(data)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

def test_forward_sequential():
keras_model = keras.models.Sequential([
Expand All @@ -136,7 +142,7 @@ def test_forward_sequential():


def test_forward_pool():
data = keras.layers.Input(shape=(32,32,1))
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)
Expand All @@ -148,36 +154,36 @@ def test_forward_pool():


def test_forward_conv():
data = keras.layers.Input(shape=(32,32,3))
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')]
data = keras.layers.Input(shape=(32, 32, 3))
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)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model)


def test_forward_upsample(interpolation='nearest'):
data = keras.layers.Input(shape=(32,32,3))
x = keras.layers.UpSampling2D(size=(3,3), interpolation=interpolation)(data)
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.UpSampling2D(size=(3, 3), interpolation=interpolation)(data)
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)
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Reshape(target_shape=(32, 32, 3))(data)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model)


def test_forward_crop():
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Cropping2D(cropping=((1, 1), (1, 1)))(data)
x = keras.layers.Cropping2D(cropping=(1, 1))(x)
x = keras.layers.Cropping2D(cropping=1)(x)
Expand All @@ -190,8 +196,8 @@ def test_forward_crop():


def test_forward_multi_inputs():
data1 = keras.layers.Input(shape=(32,32,3))
data2 = keras.layers.Input(shape=(32,32,3))
data1 = keras.layers.Input(shape=(32, 32, 3))
data2 = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Conv2D(8, (3, 3), padding="same")(data1)
y = keras.layers.Conv2D(8, (3, 3), padding="same")(data2)
z = keras.layers.Average()([x, y])
Expand All @@ -201,7 +207,7 @@ def test_forward_multi_inputs():


def test_forward_multi_outputs():
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
x = keras.layers.GlobalAveragePooling2D()(x)
y = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
Expand All @@ -212,7 +218,7 @@ def test_forward_multi_outputs():

def test_forward_reuse_layers():
# reuse conv2d
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
conv2d = keras.layers.Conv2D(8, (3, 3), padding="same")
x = conv2d(data)
y = conv2d(data)
Expand All @@ -221,7 +227,7 @@ def test_forward_reuse_layers():
keras_model = keras.models.Model(data, z)
verify_keras_frontend(keras_model)
# reuse add
data = keras.layers.Input(shape=(32,32,3))
data = keras.layers.Input(shape=(32, 32, 3))
x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
add = keras.layers.Add()
x = add([x, x])
Expand All @@ -232,7 +238,7 @@ def test_forward_reuse_layers():


def test_forward_rnn():
data = keras.layers.Input(shape=(1,32))
data = keras.layers.Input(shape=(1, 32))
rnn_funcs = [keras.layers.LSTM(units=16, return_state=False,
recurrent_activation='sigmoid', activation='tanh'),
keras.layers.SimpleRNN(units=16, return_state=False,
Expand All @@ -247,32 +253,33 @@ def test_forward_rnn():

def test_forward_vgg16():
keras_model = keras.applications.VGG16(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
input_shape=(224, 224, 3), classes=1000)
verify_keras_frontend(keras_model)


def test_forward_xception():
keras_model = keras.applications.Xception(include_top=True, weights='imagenet',
input_shape=(299,299,3), classes=1000)
input_shape=(299, 299, 3), classes=1000)
verify_keras_frontend(keras_model)


def test_forward_resnet50():
keras_model = keras.applications.ResNet50(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
input_shape=(224, 224, 3), classes=1000)
verify_keras_frontend(keras_model)


def test_forward_mobilenet():
keras_model = keras.applications.MobileNet(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
input_shape=(224, 224, 3), classes=1000)
verify_keras_frontend(keras_model)


if __name__ == '__main__':
test_forward_merge()
test_forward_activations()
test_forward_dense()
test_forward_permute()
test_forward_sequential()
test_forward_pool()
test_forward_conv()
Expand Down