Add OpenFace to nightly build

applications/nightly_build/test_open_face.py

@@ -0,0 +1,275 @@
+###############################################################################
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+###############################################################################
+import os
+import sys
+import unittest
+import keras2onnx
+import numpy as np
+from keras2onnx.proto import keras, is_tf_keras
+from os.path import dirname, abspath
+sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../tests/'))
+from test_utils import run_onnx_runtime
+import tensorflow as tf
+
+Activation = keras.layers.Activation
+AveragePooling2D = keras.layers.AveragePooling2D
+BatchNormalization = keras.layers.BatchNormalization
+concatenate = keras.layers.concatenate
+Conv2D = keras.layers.Conv2D
+Dense = keras.layers.Dense
+Dropout = keras.layers.Dropout
+Embedding = keras.layers.Embedding
+Flatten = keras.layers.Flatten
+Input = keras.layers.Input
+Lambda = keras.layers.Lambda
+LeakyReLU = keras.layers.LeakyReLU
+MaxPooling2D = keras.layers.MaxPooling2D
+multiply = keras.layers.multiply
+Reshape = keras.layers.Reshape
+UpSampling2D = keras.layers.UpSampling2D
+ZeroPadding2D = keras.layers.ZeroPadding2D
+
+Sequential = keras.models.Sequential
+Model = keras.models.Model
+K = keras.backend
+
+# From https://github.com/serengil/deepface/blob/master/deepface/basemodels/OpenFace.py
+def loadModel():
+	myInput = Input(shape=(96, 96, 3))
+
+	x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
+	x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
+	x = Activation('relu')(x)
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = MaxPooling2D(pool_size=3, strides=2)(x)
+	x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_1')(x)
+	x = Conv2D(64, (1, 1), name='conv2')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
+	x = Activation('relu')(x)
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = Conv2D(192, (3, 3), name='conv3')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
+	x = Activation('relu')(x)
+	x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x) #x is equal added
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = MaxPooling2D(pool_size=3, strides=2)(x)
+
+	# Inception3a
+	inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
+	inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
+	inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
+	inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
+	inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
+	inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
+	inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
+
+	inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
+	inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
+	inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
+	inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
+	inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
+	inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
+	inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
+
+	inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
+	inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
+	inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
+	inception_3a_pool = Activation('relu')(inception_3a_pool)
+	inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
+
+	inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
+	inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
+	inception_3a_1x1 = Activation('relu')(inception_3a_1x1)
+
+	inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)
+
+	# Inception3b
+	inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
+	inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
+	inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
+	inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
+	inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
+	inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
+	inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
+
+	inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
+	inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
+	inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
+	inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
+	inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
+	inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
+	inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
+
+	inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
+	inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
+	inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
+	inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
+	inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
+	inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
+	inception_3b_pool = Activation('relu')(inception_3b_pool)
+	inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
+
+	inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
+	inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
+	inception_3b_1x1 = Activation('relu')(inception_3b_1x1)
+
+	inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)
+
+	# Inception3c
+	inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name='inception_3c_3x3_conv1')(inception_3b)
+	inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn1')(inception_3c_3x3)
+	inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
+	inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
+	inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name='inception_3c_3x3_conv'+'2')(inception_3c_3x3)
+	inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn'+'2')(inception_3c_3x3)
+	inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
+
+	inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name='inception_3c_5x5_conv1')(inception_3b)
+	inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn1')(inception_3c_5x5)
+	inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
+	inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
+	inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name='inception_3c_5x5_conv'+'2')(inception_3c_5x5)
+	inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn'+'2')(inception_3c_5x5)
+	inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
+
+	inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
+	inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
+
+	inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
+
+	#inception 4a
+	inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name='inception_4a_3x3_conv'+'1')(inception_3c)
+	inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'1')(inception_4a_3x3)
+	inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
+	inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
+	inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name='inception_4a_3x3_conv'+'2')(inception_4a_3x3)
+	inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'2')(inception_4a_3x3)
+	inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
+
+	inception_4a_5x5 = Conv2D(32, (1,1), strides=(1,1), name='inception_4a_5x5_conv1')(inception_3c)
+	inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn1')(inception_4a_5x5)
+	inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
+	inception_4a_5x5 = ZeroPadding2D(padding=(2,2))(inception_4a_5x5)
+	inception_4a_5x5 = Conv2D(64, (5,5), strides=(1,1), name='inception_4a_5x5_conv'+'2')(inception_4a_5x5)
+	inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn'+'2')(inception_4a_5x5)
+	inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
+
+	inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
+	inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
+	inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
+	inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
+
+	inception_4a_pool = Conv2D(128, (1,1), strides=(1,1), name='inception_4a_pool_conv'+'')(inception_4a_pool)
+	inception_4a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_pool_bn'+'')(inception_4a_pool)
+	inception_4a_pool = Activation('relu')(inception_4a_pool)
+	inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
+
+	inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name='inception_4a_1x1_conv'+'')(inception_3c)
+	inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_1x1_bn'+'')(inception_4a_1x1)
+	inception_4a_1x1 = Activation('relu')(inception_4a_1x1)
+
+	inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)
+
+	#inception4e
+	inception_4e_3x3 = Conv2D(160, (1,1), strides=(1,1), name='inception_4e_3x3_conv'+'1')(inception_4a)
+	inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'1')(inception_4e_3x3)
+	inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
+	inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
+	inception_4e_3x3 = Conv2D(256, (3,3), strides=(2,2), name='inception_4e_3x3_conv'+'2')(inception_4e_3x3)
+	inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'2')(inception_4e_3x3)
+	inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
+
+	inception_4e_5x5 = Conv2D(64, (1,1), strides=(1,1), name='inception_4e_5x5_conv'+'1')(inception_4a)
+	inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'1')(inception_4e_5x5)
+	inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
+	inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
+	inception_4e_5x5 = Conv2D(128, (5,5), strides=(2,2), name='inception_4e_5x5_conv'+'2')(inception_4e_5x5)
+	inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'2')(inception_4e_5x5)
+	inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
+
+	inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
+	inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
+
+	inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
+
+	#inception5a
+	inception_5a_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_3x3_conv'+'1')(inception_4e)
+	inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'1')(inception_5a_3x3)
+	inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
+	inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
+	inception_5a_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5a_3x3_conv'+'2')(inception_5a_3x3)
+	inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'2')(inception_5a_3x3)
+	inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
+
+	inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
+	inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
+	inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
+	inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
+
+	inception_5a_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_pool_conv'+'')(inception_5a_pool)
+	inception_5a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_pool_bn'+'')(inception_5a_pool)
+	inception_5a_pool = Activation('relu')(inception_5a_pool)
+	inception_5a_pool = ZeroPadding2D(padding=(1,1))(inception_5a_pool)
+
+	inception_5a_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5a_1x1_conv'+'')(inception_4e)
+	inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_1x1_bn'+'')(inception_5a_1x1)
+	inception_5a_1x1 = Activation('relu')(inception_5a_1x1)
+
+	inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
+
+	#inception_5b
+	inception_5b_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_3x3_conv'+'1')(inception_5a)
+	inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'1')(inception_5b_3x3)
+	inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
+	inception_5b_3x3 = ZeroPadding2D(padding=(1,1))(inception_5b_3x3)
+	inception_5b_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5b_3x3_conv'+'2')(inception_5b_3x3)
+	inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'2')(inception_5b_3x3)
+	inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
+
+	inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
+
+	inception_5b_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_pool_conv'+'')(inception_5b_pool)
+	inception_5b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_pool_bn'+'')(inception_5b_pool)
+	inception_5b_pool = Activation('relu')(inception_5b_pool)
+
+	inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
+
+	inception_5b_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5b_1x1_conv'+'')(inception_5a)
+	inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_1x1_bn'+'')(inception_5b_1x1)
+	inception_5b_1x1 = Activation('relu')(inception_5b_1x1)
+
+	inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
+
+	av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
+	reshape_layer = Flatten()(av_pool)
+	dense_layer = Dense(128, name='dense_layer')(reshape_layer)
+	norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)
+
+	# Final Model
+	model = Model(inputs=[myInput], outputs=norm_layer)
+
+	return model
+
+class TestOpenFace(unittest.TestCase):
+
+    def setUp(self):
+        self.model_files = []
+
+    def tearDown(self):
+        for fl in self.model_files:
+            os.remove(fl)
+
+    def test_OpenFace(self):
+        keras_model = loadModel()
+        x = np.random.rand(2, 96, 96, 3).astype(np.float32)
+        expected = keras_model.predict(x)
+        onnx_model = keras2onnx.convert_keras(keras_model, keras_model.name)
+        self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, x, expected, self.model_files))
+
+
+if __name__ == "__main__":
+    unittest.main()

keras2onnx/_builtin.py

@@ -1055,6 +1055,21 @@ def convert_tf_logsoftmax(scope, operator, container):
                               axis=axis)
 
 
+@converter_func(TYPES.LRN)
+def convert_tf_lrn(scope, operator, container):
+    node = operator.raw_operator
+    attrs = _to_onnx_attrs(node)
+    attrs['size'] = 2 * attrs['depth_radius'] + 1
+    del attrs['depth_radius']
+    oopb = OnnxOperatorBuilder(container, scope)
+    oopb.add_node_with_output('LRN',
+                              operator.input_full_names,
+                              operator.output_full_names,
+                              name=operator.full_name,
+                              op_version=1,
+                              **attrs)
+
+
 def _convert_tf_maximum_minimum(scope, operator, container, oopb, apply_func):
     node = operator.raw_operator
     supported_types = [oopb.double, oopb.float, oopb.float16]

keras2onnx/_consts.py

@@ -50,6 +50,7 @@ class TYPES:
     LogicalAnd = 'LogicalAnd'
     LogicalNot = 'LogicalNot'
     LogSoftmax = 'LogSoftmax'
+    LRN = 'LRN'
     MatMul = 'MatMul'
     Max = 'Max'
     Maximum = 'Maximum'