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Add OpenFace to nightly build (#672)
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| ############################################################################### | ||
| # 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 | ||
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| 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 | ||
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| Sequential = keras.models.Sequential | ||
| Model = keras.models.Model | ||
| K = keras.backend | ||
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| # From https://github.com/serengil/deepface/blob/master/deepface/basemodels/OpenFace.py | ||
| def loadModel(): | ||
| myInput = Input(shape=(96, 96, 3)) | ||
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| 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) | ||
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| # 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3) | ||
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| # 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3) | ||
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| # 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) | ||
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| 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) | ||
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| inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b) | ||
| inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool) | ||
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| inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3) | ||
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| #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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3) | ||
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| #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) | ||
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| 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) | ||
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| inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a) | ||
| inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool) | ||
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| inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3) | ||
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| #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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3) | ||
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| #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) | ||
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| inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a) | ||
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| 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) | ||
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| inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool) | ||
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| 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) | ||
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| inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3) | ||
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| 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) | ||
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| # Final Model | ||
| model = Model(inputs=[myInput], outputs=norm_layer) | ||
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| return model | ||
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| class TestOpenFace(unittest.TestCase): | ||
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| 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() |
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