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feature_map_generators_test.py

Source:feature_map_generators_test.py

1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for feature map generators."""16from absl.testing import parameterized17import numpy as np18import tensorflow as tf19from google.protobuf import text_format20from object_detection.builders import hyperparams_builder21from object_detection.models import feature_map_generators22from object_detection.protos import hyperparams_pb223INCEPTION_V2_LAYOUT = {24    'from_layer': ['Mixed_3c', 'Mixed_4c', 'Mixed_5c', '', '', ''],25    'layer_depth': [-1, -1, -1, 512, 256, 256],26    'anchor_strides': [16, 32, 64, -1, -1, -1],27    'layer_target_norm': [20.0, -1, -1, -1, -1, -1],28}29INCEPTION_V3_LAYOUT = {30    'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],31    'layer_depth': [-1, -1, -1, 512, 256, 128],32    'anchor_strides': [16, 32, 64, -1, -1, -1],33    'aspect_ratios': [1.0, 2.0, 1.0/2, 3.0, 1.0/3]34}35EMBEDDED_SSD_MOBILENET_V1_LAYOUT = {36    'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '', ''],37    'layer_depth': [-1, -1, 512, 256, 256],38    'conv_kernel_size': [-1, -1, 3, 3, 2],39}40SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT = {41    'from_layer': ['Conv2d_13_pointwise', '', '', ''],42    'layer_depth': [-1, 256, 256, 256],43}44@parameterized.parameters(45    {'use_keras': False},46    {'use_keras': True},47)48class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):49  def _build_conv_hyperparams(self):50    conv_hyperparams = hyperparams_pb2.Hyperparams()51    conv_hyperparams_text_proto = """52      regularizer {53        l2_regularizer {54        }55      }56      initializer {57        truncated_normal_initializer {58        }59      }60    """61    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)62    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)63  def _build_feature_map_generator(self, feature_map_layout, use_keras,64                                   pool_residual=False):65    if use_keras:66      return feature_map_generators.KerasMultiResolutionFeatureMaps(67          feature_map_layout=feature_map_layout,68          depth_multiplier=1,69          min_depth=32,70          insert_1x1_conv=True,71          freeze_batchnorm=False,72          is_training=True,73          conv_hyperparams=self._build_conv_hyperparams(),74          name='FeatureMaps'75      )76    else:77      def feature_map_generator(image_features):78        return feature_map_generators.multi_resolution_feature_maps(79            feature_map_layout=feature_map_layout,80            depth_multiplier=1,81            min_depth=32,82            insert_1x1_conv=True,83            image_features=image_features,84            pool_residual=pool_residual)85      return feature_map_generator86  def test_get_expected_feature_map_shapes_with_inception_v2(self, use_keras):87    image_features = {88        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),89        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),90        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)91    }92    feature_map_generator = self._build_feature_map_generator(93        feature_map_layout=INCEPTION_V2_LAYOUT,94        use_keras=use_keras95    )96    feature_maps = feature_map_generator(image_features)97    expected_feature_map_shapes = {98        'Mixed_3c': (4, 28, 28, 256),99        'Mixed_4c': (4, 14, 14, 576),100        'Mixed_5c': (4, 7, 7, 1024),101        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),102        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),103        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}104    init_op = tf.global_variables_initializer()105    with self.test_session() as sess:106      sess.run(init_op)107      out_feature_maps = sess.run(feature_maps)108      out_feature_map_shapes = dict(109          (key, value.shape) for key, value in out_feature_maps.items())110      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)111  def test_get_expected_feature_map_shapes_with_inception_v2_use_depthwise(112      self, use_keras):113    image_features = {114        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),115        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),116        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)117    }118    layout_copy = INCEPTION_V2_LAYOUT.copy()119    layout_copy['use_depthwise'] = True120    feature_map_generator = self._build_feature_map_generator(121        feature_map_layout=layout_copy,122        use_keras=use_keras123    )124    feature_maps = feature_map_generator(image_features)125    expected_feature_map_shapes = {126        'Mixed_3c': (4, 28, 28, 256),127        'Mixed_4c': (4, 14, 14, 576),128        'Mixed_5c': (4, 7, 7, 1024),129        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),130        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),131        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}132    init_op = tf.global_variables_initializer()133    with self.test_session() as sess:134      sess.run(init_op)135      out_feature_maps = sess.run(feature_maps)136      out_feature_map_shapes = dict(137          (key, value.shape) for key, value in out_feature_maps.items())138      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)139  def test_get_expected_feature_map_shapes_use_explicit_padding(140      self, use_keras):141    image_features = {142        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),143        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),144        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)145    }146    layout_copy = INCEPTION_V2_LAYOUT.copy()147    layout_copy['use_explicit_padding'] = True148    feature_map_generator = self._build_feature_map_generator(149        feature_map_layout=layout_copy,150        use_keras=use_keras151    )152    feature_maps = feature_map_generator(image_features)153    expected_feature_map_shapes = {154        'Mixed_3c': (4, 28, 28, 256),155        'Mixed_4c': (4, 14, 14, 576),156        'Mixed_5c': (4, 7, 7, 1024),157        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),158        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),159        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}160    init_op = tf.global_variables_initializer()161    with self.test_session() as sess:162      sess.run(init_op)163      out_feature_maps = sess.run(feature_maps)164      out_feature_map_shapes = dict(165          (key, value.shape) for key, value in out_feature_maps.items())166      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)167  def test_get_expected_feature_map_shapes_with_inception_v3(self, use_keras):168    image_features = {169        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),170        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),171        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)172    }173    feature_map_generator = self._build_feature_map_generator(174        feature_map_layout=INCEPTION_V3_LAYOUT,175        use_keras=use_keras176    )177    feature_maps = feature_map_generator(image_features)178    expected_feature_map_shapes = {179        'Mixed_5d': (4, 35, 35, 256),180        'Mixed_6e': (4, 17, 17, 576),181        'Mixed_7c': (4, 8, 8, 1024),182        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),183        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),184        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}185    init_op = tf.global_variables_initializer()186    with self.test_session() as sess:187      sess.run(init_op)188      out_feature_maps = sess.run(feature_maps)189      out_feature_map_shapes = dict(190          (key, value.shape) for key, value in out_feature_maps.items())191      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)192  def test_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(193      self, use_keras):194    image_features = {195        'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],196                                                 dtype=tf.float32),197        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],198                                                 dtype=tf.float32),199    }200    feature_map_generator = self._build_feature_map_generator(201        feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,202        use_keras=use_keras203    )204    feature_maps = feature_map_generator(image_features)205    expected_feature_map_shapes = {206        'Conv2d_11_pointwise': (4, 16, 16, 512),207        'Conv2d_13_pointwise': (4, 8, 8, 1024),208        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),209        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),210        'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}211    init_op = tf.global_variables_initializer()212    with self.test_session() as sess:213      sess.run(init_op)214      out_feature_maps = sess.run(feature_maps)215      out_feature_map_shapes = dict(216          (key, value.shape) for key, value in out_feature_maps.items())217      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)218  def test_feature_map_shapes_with_pool_residual_ssd_mobilenet_v1(219      self, use_keras):220    image_features = {221        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],222                                                 dtype=tf.float32),223    }224    feature_map_generator = self._build_feature_map_generator(225        feature_map_layout=SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT,226        use_keras=use_keras,227        pool_residual=True228    )229    feature_maps = feature_map_generator(image_features)230    expected_feature_map_shapes = {231        'Conv2d_13_pointwise': (4, 8, 8, 1024),232        'Conv2d_13_pointwise_2_Conv2d_1_3x3_s2_256': (4, 4, 4, 256),233        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_256': (4, 2, 2, 256),234        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 1, 1, 256)}235    init_op = tf.global_variables_initializer()236    with self.test_session() as sess:237      sess.run(init_op)238      out_feature_maps = sess.run(feature_maps)239      out_feature_map_shapes = dict(240          (key, value.shape) for key, value in out_feature_maps.items())241      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)242  def test_get_expected_variable_names_with_inception_v2(self, use_keras):243    image_features = {244        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),245        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),246        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)247    }248    feature_map_generator = self._build_feature_map_generator(249        feature_map_layout=INCEPTION_V2_LAYOUT,250        use_keras=use_keras251    )252    feature_maps = feature_map_generator(image_features)253    expected_slim_variables = set([254        'Mixed_5c_1_Conv2d_3_1x1_256/weights',255        'Mixed_5c_1_Conv2d_3_1x1_256/biases',256        'Mixed_5c_2_Conv2d_3_3x3_s2_512/weights',257        'Mixed_5c_2_Conv2d_3_3x3_s2_512/biases',258        'Mixed_5c_1_Conv2d_4_1x1_128/weights',259        'Mixed_5c_1_Conv2d_4_1x1_128/biases',260        'Mixed_5c_2_Conv2d_4_3x3_s2_256/weights',261        'Mixed_5c_2_Conv2d_4_3x3_s2_256/biases',262        'Mixed_5c_1_Conv2d_5_1x1_128/weights',263        'Mixed_5c_1_Conv2d_5_1x1_128/biases',264        'Mixed_5c_2_Conv2d_5_3x3_s2_256/weights',265        'Mixed_5c_2_Conv2d_5_3x3_s2_256/biases',266    ])267    expected_keras_variables = set([268        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/kernel',269        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/bias',270        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/kernel',271        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/bias',272        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/kernel',273        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/bias',274        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/kernel',275        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/bias',276        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/kernel',277        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/bias',278        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/kernel',279        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',280    ])281    init_op = tf.global_variables_initializer()282    with self.test_session() as sess:283      sess.run(init_op)284      sess.run(feature_maps)285      actual_variable_set = set(286          [var.op.name for var in tf.trainable_variables()])287      if use_keras:288        self.assertSetEqual(expected_keras_variables, actual_variable_set)289      else:290        self.assertSetEqual(expected_slim_variables, actual_variable_set)291  def test_get_expected_variable_names_with_inception_v2_use_depthwise(292      self,293      use_keras):294    image_features = {295        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),296        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),297        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)298    }299    layout_copy = INCEPTION_V2_LAYOUT.copy()300    layout_copy['use_depthwise'] = True301    feature_map_generator = self._build_feature_map_generator(302        feature_map_layout=layout_copy,303        use_keras=use_keras304    )305    feature_maps = feature_map_generator(image_features)306    expected_slim_variables = set([307        'Mixed_5c_1_Conv2d_3_1x1_256/weights',308        'Mixed_5c_1_Conv2d_3_1x1_256/biases',309        'Mixed_5c_2_Conv2d_3_3x3_s2_512_depthwise/depthwise_weights',310        'Mixed_5c_2_Conv2d_3_3x3_s2_512_depthwise/biases',311        'Mixed_5c_2_Conv2d_3_3x3_s2_512/weights',312        'Mixed_5c_2_Conv2d_3_3x3_s2_512/biases',313        'Mixed_5c_1_Conv2d_4_1x1_128/weights',314        'Mixed_5c_1_Conv2d_4_1x1_128/biases',315        'Mixed_5c_2_Conv2d_4_3x3_s2_256_depthwise/depthwise_weights',316        'Mixed_5c_2_Conv2d_4_3x3_s2_256_depthwise/biases',317        'Mixed_5c_2_Conv2d_4_3x3_s2_256/weights',318        'Mixed_5c_2_Conv2d_4_3x3_s2_256/biases',319        'Mixed_5c_1_Conv2d_5_1x1_128/weights',320        'Mixed_5c_1_Conv2d_5_1x1_128/biases',321        'Mixed_5c_2_Conv2d_5_3x3_s2_256_depthwise/depthwise_weights',322        'Mixed_5c_2_Conv2d_5_3x3_s2_256_depthwise/biases',323        'Mixed_5c_2_Conv2d_5_3x3_s2_256/weights',324        'Mixed_5c_2_Conv2d_5_3x3_s2_256/biases',325    ])326    expected_keras_variables = set([327        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/kernel',328        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/bias',329        ('FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_depthwise_conv/'330         'depthwise_kernel'),331        ('FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_depthwise_conv/'332         'bias'),333        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/kernel',334        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/bias',335        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/kernel',336        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/bias',337        ('FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_depthwise_conv/'338         'depthwise_kernel'),339        ('FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_depthwise_conv/'340         'bias'),341        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/kernel',342        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/bias',343        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/kernel',344        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/bias',345        ('FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_depthwise_conv/'346         'depthwise_kernel'),347        ('FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_depthwise_conv/'348         'bias'),349        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/kernel',350        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',351    ])352    init_op = tf.global_variables_initializer()353    with self.test_session() as sess:354      sess.run(init_op)355      sess.run(feature_maps)356      actual_variable_set = set(357          [var.op.name for var in tf.trainable_variables()])358      if use_keras:359        self.assertSetEqual(expected_keras_variables, actual_variable_set)360      else:361        self.assertSetEqual(expected_slim_variables, actual_variable_set)362@parameterized.parameters({'use_native_resize_op': True, 'use_keras': False},363                          {'use_native_resize_op': False, 'use_keras': False},364                          {'use_native_resize_op': True, 'use_keras': True},365                          {'use_native_resize_op': False, 'use_keras': True})366class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):367  def _build_conv_hyperparams(self):368    conv_hyperparams = hyperparams_pb2.Hyperparams()369    conv_hyperparams_text_proto = """370      regularizer {371        l2_regularizer {372        }373      }374      initializer {375        truncated_normal_initializer {376        }377      }378    """379    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)380    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)381  def _build_feature_map_generator(382      self, image_features, depth, use_keras, use_bounded_activations=False,383      use_native_resize_op=False, use_explicit_padding=False,384      use_depthwise=False):385    if use_keras:386      return feature_map_generators.KerasFpnTopDownFeatureMaps(387          num_levels=len(image_features),388          depth=depth,389          is_training=True,390          conv_hyperparams=self._build_conv_hyperparams(),391          freeze_batchnorm=False,392          use_depthwise=use_depthwise,393          use_explicit_padding=use_explicit_padding,394          use_bounded_activations=use_bounded_activations,395          use_native_resize_op=use_native_resize_op,396          scope=None,397          name='FeatureMaps',398      )399    else:400      def feature_map_generator(image_features):401        return feature_map_generators.fpn_top_down_feature_maps(402            image_features=image_features,403            depth=depth,404            use_depthwise=use_depthwise,405            use_explicit_padding=use_explicit_padding,406            use_bounded_activations=use_bounded_activations,407            use_native_resize_op=use_native_resize_op)408      return feature_map_generator409  def test_get_expected_feature_map_shapes(410      self, use_native_resize_op, use_keras):411    image_features = [412        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),413        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),414        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),415        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))416    ]417    feature_map_generator = self._build_feature_map_generator(418        image_features=image_features,419        depth=128,420        use_keras=use_keras,421        use_native_resize_op=use_native_resize_op)422    feature_maps = feature_map_generator(image_features)423    expected_feature_map_shapes = {424        'top_down_block2': (4, 8, 8, 128),425        'top_down_block3': (4, 4, 4, 128),426        'top_down_block4': (4, 2, 2, 128),427        'top_down_block5': (4, 1, 1, 128)428    }429    init_op = tf.global_variables_initializer()430    with self.test_session() as sess:431      sess.run(init_op)432      out_feature_maps = sess.run(feature_maps)433      out_feature_map_shapes = {key: value.shape434                                for key, value in out_feature_maps.items()}435      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)436  def test_get_expected_feature_map_shapes_with_explicit_padding(437      self, use_native_resize_op, use_keras):438    image_features = [439        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),440        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),441        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),442        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))443    ]444    feature_map_generator = self._build_feature_map_generator(445        image_features=image_features,446        depth=128,447        use_keras=use_keras,448        use_explicit_padding=True,449        use_native_resize_op=use_native_resize_op)450    feature_maps = feature_map_generator(image_features)451    expected_feature_map_shapes = {452        'top_down_block2': (4, 8, 8, 128),453        'top_down_block3': (4, 4, 4, 128),454        'top_down_block4': (4, 2, 2, 128),455        'top_down_block5': (4, 1, 1, 128)456    }457    init_op = tf.global_variables_initializer()458    with self.test_session() as sess:459      sess.run(init_op)460      out_feature_maps = sess.run(feature_maps)461      out_feature_map_shapes = {key: value.shape462                                for key, value in out_feature_maps.items()}463      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)464  def test_use_bounded_activations_add_operations(465      self, use_native_resize_op, use_keras):466    tf_graph = tf.Graph()467    with tf_graph.as_default():468      image_features = [('block2',469                         tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),470                        ('block3',471                         tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),472                        ('block4',473                         tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),474                        ('block5',475                         tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))]476      feature_map_generator = self._build_feature_map_generator(477          image_features=image_features,478          depth=128,479          use_keras=use_keras,480          use_bounded_activations=True,481          use_native_resize_op=use_native_resize_op)482      feature_map_generator(image_features)483      if use_keras:484        expected_added_operations = dict.fromkeys([485            'FeatureMaps/top_down/clip_by_value/clip_by_value',486            'FeatureMaps/top_down/clip_by_value_1/clip_by_value',487            'FeatureMaps/top_down/clip_by_value_2/clip_by_value',488            'FeatureMaps/top_down/clip_by_value_3/clip_by_value',489            'FeatureMaps/top_down/clip_by_value_4/clip_by_value',490            'FeatureMaps/top_down/clip_by_value_5/clip_by_value',491            'FeatureMaps/top_down/clip_by_value_6/clip_by_value',492        ])493      else:494        expected_added_operations = dict.fromkeys([495            'top_down/clip_by_value', 'top_down/clip_by_value_1',496            'top_down/clip_by_value_2', 'top_down/clip_by_value_3',497            'top_down/clip_by_value_4', 'top_down/clip_by_value_5',498            'top_down/clip_by_value_6'499        ])500      op_names = {op.name: None for op in tf_graph.get_operations()}501      self.assertDictContainsSubset(expected_added_operations, op_names)502  def test_use_bounded_activations_clip_value(503      self, use_native_resize_op, use_keras):504    tf_graph = tf.Graph()505    with tf_graph.as_default():506      image_features = [507          ('block2', 255 * tf.ones([4, 8, 8, 256], dtype=tf.float32)),508          ('block3', 255 * tf.ones([4, 4, 4, 256], dtype=tf.float32)),509          ('block4', 255 * tf.ones([4, 2, 2, 256], dtype=tf.float32)),510          ('block5', 255 * tf.ones([4, 1, 1, 256], dtype=tf.float32))511      ]512      feature_map_generator = self._build_feature_map_generator(513          image_features=image_features,514          depth=128,515          use_keras=use_keras,516          use_bounded_activations=True,517          use_native_resize_op=use_native_resize_op)518      feature_map_generator(image_features)519      if use_keras:520        expected_clip_by_value_ops = dict.fromkeys([521            'FeatureMaps/top_down/clip_by_value/clip_by_value',522            'FeatureMaps/top_down/clip_by_value_1/clip_by_value',523            'FeatureMaps/top_down/clip_by_value_2/clip_by_value',524            'FeatureMaps/top_down/clip_by_value_3/clip_by_value',525            'FeatureMaps/top_down/clip_by_value_4/clip_by_value',526            'FeatureMaps/top_down/clip_by_value_5/clip_by_value',527            'FeatureMaps/top_down/clip_by_value_6/clip_by_value',528        ])529      else:530        expected_clip_by_value_ops = [531            'top_down/clip_by_value', 'top_down/clip_by_value_1',532            'top_down/clip_by_value_2', 'top_down/clip_by_value_3',533            'top_down/clip_by_value_4', 'top_down/clip_by_value_5',534            'top_down/clip_by_value_6'535        ]536      # Gathers activation tensors before and after clip_by_value operations.537      activations = {}538      for clip_by_value_op in expected_clip_by_value_ops:539        clip_input_tensor = tf_graph.get_operation_by_name(540            '{}/Minimum'.format(clip_by_value_op)).inputs[0]541        clip_output_tensor = tf_graph.get_tensor_by_name(542            '{}:0'.format(clip_by_value_op))543        activations.update({544            'before_{}'.format(clip_by_value_op): clip_input_tensor,545            'after_{}'.format(clip_by_value_op): clip_output_tensor,546        })547      expected_lower_bound = -feature_map_generators.ACTIVATION_BOUND548      expected_upper_bound = feature_map_generators.ACTIVATION_BOUND549      init_op = tf.global_variables_initializer()550      with self.test_session() as session:551        session.run(init_op)552        activations_output = session.run(activations)553        for clip_by_value_op in expected_clip_by_value_ops:554          # Before clipping, activations are beyound the expected bound because555          # of large input image_features values.556          activations_before_clipping = (557              activations_output['before_{}'.format(clip_by_value_op)])558          before_clipping_lower_bound = np.amin(activations_before_clipping)559          before_clipping_upper_bound = np.amax(activations_before_clipping)560          self.assertLessEqual(before_clipping_lower_bound,561                               expected_lower_bound)562          self.assertGreaterEqual(before_clipping_upper_bound,563                                  expected_upper_bound)564          # After clipping, activations are bounded as expectation.565          activations_after_clipping = (566              activations_output['after_{}'.format(clip_by_value_op)])567          after_clipping_lower_bound = np.amin(activations_after_clipping)568          after_clipping_upper_bound = np.amax(activations_after_clipping)569          self.assertGreaterEqual(after_clipping_lower_bound,570                                  expected_lower_bound)571          self.assertLessEqual(after_clipping_upper_bound, expected_upper_bound)572  def test_get_expected_feature_map_shapes_with_depthwise(573      self, use_native_resize_op, use_keras):574    image_features = [575        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),576        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),577        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),578        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))579    ]580    feature_map_generator = self._build_feature_map_generator(581        image_features=image_features,582        depth=128,583        use_keras=use_keras,584        use_depthwise=True,585        use_native_resize_op=use_native_resize_op)586    feature_maps = feature_map_generator(image_features)587    expected_feature_map_shapes = {588        'top_down_block2': (4, 8, 8, 128),589        'top_down_block3': (4, 4, 4, 128),590        'top_down_block4': (4, 2, 2, 128),591        'top_down_block5': (4, 1, 1, 128)592    }593    init_op = tf.global_variables_initializer()594    with self.test_session() as sess:595      sess.run(init_op)596      out_feature_maps = sess.run(feature_maps)597      out_feature_map_shapes = {key: value.shape598                                for key, value in out_feature_maps.items()}599      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)600  def test_get_expected_variable_names(601      self, use_native_resize_op, use_keras):602    image_features = [603        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),604        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),605        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),606        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))607    ]608    feature_map_generator = self._build_feature_map_generator(609        image_features=image_features,610        depth=128,611        use_keras=use_keras,612        use_native_resize_op=use_native_resize_op)613    feature_maps = feature_map_generator(image_features)614    expected_slim_variables = set([615        'projection_1/weights',616        'projection_1/biases',617        'projection_2/weights',618        'projection_2/biases',619        'projection_3/weights',620        'projection_3/biases',621        'projection_4/weights',622        'projection_4/biases',623        'smoothing_1/weights',624        'smoothing_1/biases',625        'smoothing_2/weights',626        'smoothing_2/biases',627        'smoothing_3/weights',628        'smoothing_3/biases',629    ])630    expected_keras_variables = set([631        'FeatureMaps/top_down/projection_1/kernel',632        'FeatureMaps/top_down/projection_1/bias',633        'FeatureMaps/top_down/projection_2/kernel',634        'FeatureMaps/top_down/projection_2/bias',635        'FeatureMaps/top_down/projection_3/kernel',636        'FeatureMaps/top_down/projection_3/bias',637        'FeatureMaps/top_down/projection_4/kernel',638        'FeatureMaps/top_down/projection_4/bias',639        'FeatureMaps/top_down/smoothing_1_conv/kernel',640        'FeatureMaps/top_down/smoothing_1_conv/bias',641        'FeatureMaps/top_down/smoothing_2_conv/kernel',642        'FeatureMaps/top_down/smoothing_2_conv/bias',643        'FeatureMaps/top_down/smoothing_3_conv/kernel',644        'FeatureMaps/top_down/smoothing_3_conv/bias'645    ])646    init_op = tf.global_variables_initializer()647    with self.test_session() as sess:648      sess.run(init_op)649      sess.run(feature_maps)650      actual_variable_set = set(651          [var.op.name for var in tf.trainable_variables()])652      if use_keras:653        self.assertSetEqual(expected_keras_variables, actual_variable_set)654      else:655        self.assertSetEqual(expected_slim_variables, actual_variable_set)656  def test_get_expected_variable_names_with_depthwise(657      self, use_native_resize_op, use_keras):658    image_features = [659        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),660        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),661        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),662        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))663    ]664    feature_map_generator = self._build_feature_map_generator(665        image_features=image_features,666        depth=128,667        use_keras=use_keras,668        use_depthwise=True,669        use_native_resize_op=use_native_resize_op)670    feature_maps = feature_map_generator(image_features)671    expected_slim_variables = set([672        'projection_1/weights',673        'projection_1/biases',674        'projection_2/weights',675        'projection_2/biases',676        'projection_3/weights',677        'projection_3/biases',678        'projection_4/weights',679        'projection_4/biases',680        'smoothing_1/depthwise_weights',681        'smoothing_1/pointwise_weights',682        'smoothing_1/biases',683        'smoothing_2/depthwise_weights',684        'smoothing_2/pointwise_weights',685        'smoothing_2/biases',686        'smoothing_3/depthwise_weights',687        'smoothing_3/pointwise_weights',688        'smoothing_3/biases',689    ])690    expected_keras_variables = set([691        'FeatureMaps/top_down/projection_1/kernel',692        'FeatureMaps/top_down/projection_1/bias',693        'FeatureMaps/top_down/projection_2/kernel',694        'FeatureMaps/top_down/projection_2/bias',695        'FeatureMaps/top_down/projection_3/kernel',696        'FeatureMaps/top_down/projection_3/bias',697        'FeatureMaps/top_down/projection_4/kernel',698        'FeatureMaps/top_down/projection_4/bias',699        'FeatureMaps/top_down/smoothing_1_depthwise_conv/depthwise_kernel',700        'FeatureMaps/top_down/smoothing_1_depthwise_conv/pointwise_kernel',701        'FeatureMaps/top_down/smoothing_1_depthwise_conv/bias',702        'FeatureMaps/top_down/smoothing_2_depthwise_conv/depthwise_kernel',703        'FeatureMaps/top_down/smoothing_2_depthwise_conv/pointwise_kernel',704        'FeatureMaps/top_down/smoothing_2_depthwise_conv/bias',705        'FeatureMaps/top_down/smoothing_3_depthwise_conv/depthwise_kernel',706        'FeatureMaps/top_down/smoothing_3_depthwise_conv/pointwise_kernel',707        'FeatureMaps/top_down/smoothing_3_depthwise_conv/bias'708    ])709    init_op = tf.global_variables_initializer()710    with self.test_session() as sess:711      sess.run(init_op)712      sess.run(feature_maps)713      actual_variable_set = set(714          [var.op.name for var in tf.trainable_variables()])715      if use_keras:716        self.assertSetEqual(expected_keras_variables, actual_variable_set)717      else:718        self.assertSetEqual(expected_slim_variables, actual_variable_set)719class GetDepthFunctionTest(tf.test.TestCase):720  def test_return_min_depth_when_multiplier_is_small(self):721    depth_fn = feature_map_generators.get_depth_fn(depth_multiplier=0.5,722                                                   min_depth=16)723    self.assertEqual(depth_fn(16), 16)724  def test_return_correct_depth_with_multiplier(self):725    depth_fn = feature_map_generators.get_depth_fn(depth_multiplier=0.5,726                                                   min_depth=16)727    self.assertEqual(depth_fn(64), 32)728@parameterized.parameters(729    {'replace_pool_with_conv': False},730    {'replace_pool_with_conv': True},731)732class PoolingPyramidFeatureMapGeneratorTest(tf.test.TestCase):733  def test_get_expected_feature_map_shapes(self, replace_pool_with_conv):734    image_features = {735        'image_features': tf.random_uniform([4, 19, 19, 1024])736    }737    feature_maps = feature_map_generators.pooling_pyramid_feature_maps(738        base_feature_map_depth=1024,739        num_layers=6,740        image_features=image_features,741        replace_pool_with_conv=replace_pool_with_conv)742    expected_pool_feature_map_shapes = {743        'Base_Conv2d_1x1_1024': (4, 19, 19, 1024),744        'MaxPool2d_0_2x2': (4, 10, 10, 1024),745        'MaxPool2d_1_2x2': (4, 5, 5, 1024),746        'MaxPool2d_2_2x2': (4, 3, 3, 1024),747        'MaxPool2d_3_2x2': (4, 2, 2, 1024),748        'MaxPool2d_4_2x2': (4, 1, 1, 1024),749    }750    expected_conv_feature_map_shapes = {751        'Base_Conv2d_1x1_1024': (4, 19, 19, 1024),752        'Conv2d_0_3x3_s2_1024': (4, 10, 10, 1024),753        'Conv2d_1_3x3_s2_1024': (4, 5, 5, 1024),754        'Conv2d_2_3x3_s2_1024': (4, 3, 3, 1024),755        'Conv2d_3_3x3_s2_1024': (4, 2, 2, 1024),756        'Conv2d_4_3x3_s2_1024': (4, 1, 1, 1024),757    }758    init_op = tf.global_variables_initializer()759    with self.test_session() as sess:760      sess.run(init_op)761      out_feature_maps = sess.run(feature_maps)762      out_feature_map_shapes = {key: value.shape763                                for key, value in out_feature_maps.items()}764      if replace_pool_with_conv:765        self.assertDictEqual(expected_conv_feature_map_shapes,766                             out_feature_map_shapes)767      else:768        self.assertDictEqual(expected_pool_feature_map_shapes,769                             out_feature_map_shapes)770  def test_get_expected_variable_names(self, replace_pool_with_conv):771    image_features = {772        'image_features': tf.random_uniform([4, 19, 19, 1024])773    }774    feature_maps = feature_map_generators.pooling_pyramid_feature_maps(775        base_feature_map_depth=1024,776        num_layers=6,777        image_features=image_features,778        replace_pool_with_conv=replace_pool_with_conv)779    expected_pool_variables = set([780        'Base_Conv2d_1x1_1024/weights',781        'Base_Conv2d_1x1_1024/biases',782    ])783    expected_conv_variables = set([784        'Base_Conv2d_1x1_1024/weights',785        'Base_Conv2d_1x1_1024/biases',786        'Conv2d_0_3x3_s2_1024/weights',787        'Conv2d_0_3x3_s2_1024/biases',788        'Conv2d_1_3x3_s2_1024/weights',789        'Conv2d_1_3x3_s2_1024/biases',790        'Conv2d_2_3x3_s2_1024/weights',791        'Conv2d_2_3x3_s2_1024/biases',792        'Conv2d_3_3x3_s2_1024/weights',793        'Conv2d_3_3x3_s2_1024/biases',794        'Conv2d_4_3x3_s2_1024/weights',795        'Conv2d_4_3x3_s2_1024/biases',796    ])797    init_op = tf.global_variables_initializer()798    with self.test_session() as sess:799      sess.run(init_op)800      sess.run(feature_maps)801      actual_variable_set = set(802          [var.op.name for var in tf.trainable_variables()])803      if replace_pool_with_conv:804        self.assertSetEqual(expected_conv_variables, actual_variable_set)805      else:806        self.assertSetEqual(expected_pool_variables, actual_variable_set)807if __name__ == '__main__':...

pif_calculator.py

Source:pif_calculator.py

1import collections2import math3import operator4import random5import itertools6import pandas as pd7import numpy as np8from piflib.data_util import calculate_distribution, complete_feature_priors9from piflib.entropy import create_conditional_entropy_table10def compute_cigs(dataframe,11                 feature_priors={},12                 feature_accuracies={},13                 samples=None):14    """Compute the cell information gain (CIG) for all cells in the dataset.15    Find the risk (as KL divergence from prior) for all attributes.16    :param dataframe: a Pandas DataFrame object containing tabular data17    :param feature_priors: feature_priors are optional. It is a dictionary mapping the18        feature index to an assumed prior. If not provided, the prior for19        the feature is calculated from the global distribution.20    :param feature_accuracies: `feature_accuracies` maps the feature index to the accuracy of the21        feature. If not provided for a feature, it defaults to 1.22    :return: a Pandas DataFrame containing the CIG values. The CIG values are at the same index as their corresponding23        cell values in the input dataframe.24    """25    unknown_features = 126    dataset = dataframe.values27    num_features = len(dataset[0])28    assert all(len(row) == num_features for row in dataset)29    feature_priors = complete_feature_priors(dataframe, feature_priors)30    feature_accuracies = feature_accuracies.copy()31    for i in range(num_features):32        if i not in feature_accuracies:33            feature_accuracies[i] = 134    feature_counts = [0] * num_features35    feature_kls = [[0] * len(dataset) for _ in range(num_features)]36    for is_ in sample_is(num_features, unknown_features, samples):37        feature_kls_this = find_kls_for_features(38            dataset,39            is_,40            feature_priors,41            feature_accuracies)42        for i, feature_kl in zip(is_, feature_kls_this):43            feature_kl_previous = feature_kls[i]44            feature_kls[i] = tuple(map(45                operator.add, feature_kl, feature_kl_previous))46        for i in is_:47            feature_counts[i] += 148    for i, denom in enumerate(feature_counts):49        feature_kls[i] = tuple(map(50            operator.truediv,51            feature_kls[i],52            itertools.repeat(denom)))53    return pd.DataFrame(list(zip(*feature_kls)), columns=dataframe.columns)54def compute_weighted_cigs(dataframe, feature_priors={}, feature_accuracies={}):55    """Compute the Weighted Cell Information Gain (wCIG) for all cells in the dataset.56    Find the risk (as KL divergence from prior) for all attributes.57    :param dataframe: a Pandas DataFrame object containing tabular data58    :param feature_priors: feature_priors are optional. It is a dictionary mapping the59        feature index to an assumed prior. If not provided, the prior for60        the feature is calculated from the global distribution.61    :param feature_accuracies: `feature_accuracies` maps the feature index to the accuracy of the62        feature. If not provided for a feature, it defaults to 1.63    :return: a Pandas DataFrame containing the wCIG values. The wCIG values are at the same index as their64        corresponding cell values in the input dataframe.65        """66    cigs = compute_cigs(dataframe, feature_priors=feature_priors, feature_accuracies=feature_accuracies)67    cond_entropy = create_conditional_entropy_table(dataframe)68    weights = cond_entropy['H(X|Y)'].values / cond_entropy['H(X)']69    weights = np.nan_to_num(weights)70    return (cigs * weights).round(2)71def compute_csfs(df, feature_priors={}, feature_accuracies={}):72    """Compute the Cell Surprise Factor (CSF) for all cells in the dataset.73    The CSF id defined as the change in probability for a cell value between the prior and the posterior distribution.74    :param dataframe: a Pandas DataFrame object containing tabular data75    :param feature_priors: feature_priors are optional. It is a dictionary mapping the76        feature index to an assumed prior. If not provided, the prior for77        the feature is calculated from the global distribution.78    :param feature_accuracies: `feature_accuracies` maps the feature index to the accuracy of the79        feature. If not provided for a feature, it defaults to 1.80    :return: a Pandas DataFrame containing the CSF values. The CSF values are at the same index as their81        corresponding cell values in the input dataframe.82    """83    dataset = df.values84    num_features = len(dataset[0])85    # compute priors86    feature_priors = complete_feature_priors(df, feature_priors)87    feature_accuracies = feature_accuracies.copy()88    for i in range(num_features):89        if i not in feature_accuracies:90            feature_accuracies[i] = 191    feature_csfs = [[0] * len(dataset) for _ in range(num_features)]92    for is_ in sample_is(num_features, 1, None):93        feature_csfs[is_[0]] = apply_to_posterior_and_prior(94            dataset,95            is_,96            feature_priors,97            feature_accuracies,98            calculate_prob_change)99    return pd.DataFrame(list(zip(*feature_csfs)), columns=df.columns)100def compute_pif(cigs, percentile):101    """ compute the PIF.102    The PIF is defined as the n-th percentile of the individual RIG values. Or in other words, the RIG of n percent of103    the entities in the dataset does not exceed the PIF value.104    RIG stands for row information gain. It represents the overall information gain for an entity in the dataset. The105    RIG is computed by summing the CIG values of an entity.106    The percentile value can be chosen between 0 and 100. 100 will return the maximum RIG value. Often, the RIG values107    from a long tail distribution with few high value outliers. Choosing a percentile value lower than 100 will ignore108    (some of) the highest values. If ignoring the risk of some entities in the dataset fits within your risk framework,109    then specifying a percentile value of less than 100 will make the PIF value less susceptible to RIG outliers.110    :param cigs: The CIG values of the dataset (see the compute_cigs function in this module)111    :param percentile: Which percentile of RIG values should be included in the PIF.112    :returns: the PIF_percentile value of the given CIGs113    """114    rigs = cigs.sum(axis=1)115    pif = np.percentile(rigs, percentile)116    return pif117def compute_posterior_distributions(feature, df):118    known_features = tuple(col_name for col_name in df.columns if col_name != feature)119    bucket = collections.defaultdict(list)120    bucket_map = []121    for idx, row in df.iterrows():122        key = tuple(row[known_feature] for known_feature in known_features)123        bucket[key].append(row[feature])124        bucket_map.append(key)125    bucket_distributions = {key: calculate_distribution(el_bucket) for key, el_bucket in bucket.items()}126    feature_vals = df[feature].unique()127    dists = {}128    for key, distribution in bucket_distributions.items():129        dists[str(key)] = [distribution.get(feature_val, 0) for feature_val in feature_vals]130    return dists, feature_vals131def binom(n, r):132    """ return binomial coefficient: n choose k"""133    return math.factorial(n) // math.factorial(n - r) // math.factorial(r)134def sample_is(n, r, samples):135    if samples is None:136        yield from itertools.combinations(range(n), r)137    else:138        total_combinations = binom(n, r)139        if samples > total_combinations:140            raise ValueError('more samples than combinations')141        if samples >= total_combinations >> 1:142            all_combinations = list(itertools.combinations(range(n), r))143            random.shuffle(all_combinations)144            num_produced = 0145            feature_produced = [False] * n146            for i, comb in enumerate(all_combinations):147                if num_produced >= samples:148                    break149                if all(map(feature_produced.__getitem__, comb)):150                    continue151                for j in comb:152                    feature_produced[j] = True153                num_produced += 1154                all_combinations[i] = None155                yield comb156            for comb in all_combinations:157                if num_produced >= samples:158                    break159                if comb is not None:160                    yield comb161        else:162            already_produced = set()163            feature_produced = [False] * n164            while len(already_produced) < samples:165                comb = random.sample(range(n), r)166                comb = tuple(sorted(comb))167                if (comb not in already_produced168                        and (all(already_produced)169                             or not all(map(already_produced.__getitem__,170                                            comb)))):171                    already_produced.add(comb)172                    for i in comb:173                        feature_produced[i] = True174                    yield comb175def apply_to_posterior_and_prior(dataset, feature_idx, prior_distributions, accuracies, fun):176    num_features = len(dataset[0])177    assert all(len(row) == num_features for row in dataset)178    feature_idx = feature_idx[0]179    buckets = collections.defaultdict(list)180    bucket_map = []181    for row in dataset:182        key = tuple(row[i] for i in range(num_features) if not i == feature_idx)183        buckets[key].append(row[feature_idx])184        bucket_map.append((key, row[feature_idx]))185    bucket_values = {186            key: fun(187                calculate_distribution(bucket,188                                       accuracy=accuracies[feature_idx],189                                       feature_distribution=prior_distributions[feature_idx]),190                prior_distributions[feature_idx])191            for key, bucket in buckets.items()}192    return [bucket_values[post_key][val] for post_key, val in bucket_map]193def find_kls_for_features(dataset, feature_is, feature_distributions, accuracies):194    """Find the KL divergence of feature values against the prior.195    We find the true distribution of the features taking into account196    the accuracy. We then compute the KL divergence.197    """198    num_features = len(dataset[0])199    assert all(len(row) == num_features for row in dataset)200    # one bucket per set of 'known' features201    buckets = [collections.defaultdict(list) for _ in range(len(feature_is))]202    bucket_map = [[] for _ in range(len(feature_is))]203    for row in dataset:204        key = tuple(row[i] for i in range(num_features) if i not in feature_is)205        for i, j in enumerate(feature_is):206            buckets[i][key].append(row[j])207            bucket_map[i].append(key)208    bucket_kls = [209        {210            key: calculate_kl(211                calculate_distribution(bucket,212                                       accuracy=accuracies[feature_is[i]],213                                       feature_distribution=feature_distributions[feature_is[i]]),214                feature_distributions[feature_is[i]])215            for key, bucket in feature_buckets.items()}216        for i, feature_buckets in enumerate(buckets)]217    return [tuple(map(bucket_kls[i].__getitem__, bucket_map[i]))218            for i in range(len(feature_is))]219def calculate_kl(p, q):220    """Calculate D_KL(P || Q) (the KL-divergence) in bits.221    D_KL(P || Q) is the `information gained when one revises one's222    beliefs from the prior probability distribution Q to the posterior223    probability distribution P`. (Wikipedia, KullbackâLeibler224    divergence)225    `p` and `q` are both dictionaries mapping some hashable to a number.226    It is assumed that they are both normalised: their values should add227    up to 0. `q` must not have any 0 values unless the corresponding `p`228    value is also 0.229    """230    return sum(pk * math.log2(pk / q[k])231               for k, pk in p.items()232               if pk > 0)233def calculate_prob_change(p, q):234    """ calculate the change in probability for each element of the posterior compared to the prior"""...

trip_quad.py

Source:trip_quad.py

1from __future__ import division2import pickle3import numpy as np4import cv25import math6from sys import argv7from copy import *8from ProjectUtils import *9from GetDataPath import *10from testvgg import TestVgg11sys.path.insert(0, './')12sys.path.append('/home/xuyangf/project/ML_deliverables/Siamese_iclr17_tf-master/src/')13import network as vgg14from feature_extractor import FeatureExtractor15from utils import *16# import importlib17# importlib.reload(sys)18reload(sys)  19sys.setdefaultencoding('utf8')20cat=argv[1]21cluster_num = 25622myimage_path=LoadImage(cat)23image_path=[]24for mypath in myimage_path:25    myimg=cv2.imread(mypath, cv2.IMREAD_UNCHANGED)26    if(max(myimg.shape[0],myimg.shape[1])>100):27        image_path.append(mypath)28img_num=len(image_path)29layer_name = 'pool3'30file_path = '/data2/xuyangf/OcclusionProject/NaiveVersion/feature/feature3/L3Feature'+cat31#cluster_file = '/data2/xuyangf/OcclusionProject/NaiveVersion/cluster/clusterL3/vgg16_'+cat+'_K'+str(cluster_num)+'.pickle'32prun_file = '/data2/xuyangf/OcclusionProject/NaiveVersion/prunning/prunL3/dictionary_'+cat+'.pickle'33trip_save_file='/data2/xuyangf/OcclusionProject/NaiveVersion/vc_combinescore/3vc/cat'+cat34quad_save_file='/data2/xuyangf/OcclusionProject/NaiveVersion/vc_combinescore/4vc/cat'+cat35print('loading data...')36# number of files to read in37file_num = 1038maximg_cnt=2000039fname = file_path+str(0)+'.npz'40ff = np.load(fname)41feat_dim = ff['res'].shape[0]42img_cnt = ff['res'].shape[1]43oldimg_index=044originimage=[]45feat_set = np.zeros((feat_dim, maximg_cnt*file_num))46feat_set[:,0:img_cnt] = ff['res']47originimage+=list(ff['originpath'])48loc_dim = ff['loc_set'].shape[1]49print(loc_dim)50loc_set = np.zeros((maximg_cnt*file_num, loc_dim))51loc_set[0:img_cnt,:] = ff['loc_set']52#img_dim = ff['img_set'].shape[1:]53#img_set = np.zeros([maximg_cnt*file_num]+list(img_dim))54#img_set[0:img_cnt] = ff['img_set']55oldimg_index+=img_cnt56for ii in range(1,file_num):57    print(ii)58    fname = file_path+str(ii)+'.npz'59    ff = np.load(fname)60    originimage+=list(ff['originpath'])61    img_cnt=ff['res'].shape[1]62    print(img_cnt)63    feat_set[:,oldimg_index:(oldimg_index + img_cnt)] = ff['res']64    loc_set[oldimg_index:(oldimg_index + img_cnt),:] = ff['loc_set']65    #img_set[oldimg_index:(oldimg_index + img_cnt)] = ff['img_set']66    oldimg_index+=img_cnt67feat_set=feat_set[:,:oldimg_index]68#img_set=img_set[:oldimg_index]69loc_set=loc_set[:oldimg_index,:]70print('all feat_set')71print(feat_set.shape)72print('all img_set')73#print(img_set.shape)74#assert(len(originimage)==len(img_set))75with open(prun_file, 'rb') as fh:76    assignment, centers, _, norm = pickle.load(fh)77print('load finish')78predictor=TestVgg()79# def GetPossDecrease(original_img,occluded_img,category):80# 	originalp=predictor.getprob(original_img,category)81# 	occludedp=predictor.getprob(occluded_img,category)82# 	print('diff')83# 	print(originalp)84# 	print(occludedp)85# 	#0.3 magic number86# 	if(originalp<0.3):87# 		return -188# 	if(originalp<occludedp):89# 		return -190# 	return float(originalp-occludedp)/originalp91def GetPossDecrease(original_feature,occluded_feature,category):92	originalp=predictor.feature2result(original_feature,category)93	occludedp=predictor.feature2result(occluded_feature,category)94	print('diff')95	print(originalp)96	print(occludedp)97	#0.3 magic number98	if(originalp<0.3):99		return -1100	# if(originalp<occludedp):101	# 	return -1102	return float(originalp-occludedp)/originalp103def disttresh(input_index,cluster_center):104	thresh1=0.8105	temp_feat=feat_set[:,input_index]106	error = np.sum((temp_feat.T - cluster_center)**2, 1)107	sort_idx = np.argsort(error)108	return input_index[sort_idx[:int(thresh1*len(sort_idx))]]109fname ='/data2/xuyangf/OcclusionProject/NaiveVersion/vc_combinescore/2vc/cat'+str(cat)+'.npz'110ff=np.load(fname)111img_vc_avg=ff['vc_score']112vc_name=ff['vc']113vc_num=len(img_vc_avg)114img_vc_avg=np.array(img_vc_avg)115vc_name=np.array(vc_name)116b=np.argsort(-img_vc_avg)117trip_img_vc_score=[]118trip_img_vc=[]119quad_img_vc_score=[]120quad_img_vc=[]121topvc=b[:25]122for indexk in topvc:123	thename=vc_name[indexk]124	k1=int(thename[0])125	k2=int(thename[1])126	for indexkk in topvc:127		if indexk<=indexkk:128			continue129		thename2=vc_name[indexkk]130		k3=int(thename2[0])131		k4=int(thename2[1])132		myk=[]133		myk.append(k1)134		myk.append(k2)135		myk.append(k3)136		myk.append(k4)137		myk=set(myk)138		myk=list(myk)139		if len(myk)==3:140			trip_img_vc.append(str(myk[0])+'_'+str(myk[1])+'_'+str(myk[2]))141		else:142			quad_img_vc.append(str(myk[0])+'_'+str(myk[1])+'_'+str(myk[2])+'_'+str(myk[3]))143trip_img_vc=list(set(trip_img_vc))144quad_img_vc=list(set(quad_img_vc))145for s in trip_img_vc:146	dot1=s.find('_')147	k1=int(s[:dot1])148	s2=s[dot1+1:]149	dot2=s2.find('_')150	k2=int(s2[:dot2])151	s3=s2[dot2+1:]152	k3=int(s3)153	target1=centers[k1]154	index1=np.where(assignment==k1)[0]155	index1=disttresh(index1,target1)156	target2=centers[k2]157	index2=np.where(assignment==k2)[0]158	index2=disttresh(index2,target2)159	target3=centers[k3]160	index3=np.where(assignment==k3)[0]161	index3=disttresh(index3,target3)162	myscore=[]163	for n in range(0,img_num):164		myindex1=[]165		for i in range(len(index1)):166			if image_path[n]==originimage[index1[i]]:167				myindex1.append(index1[i])168		#myindex=OnlyTheClosest(myindex,target), or other preprocessing method169		myindex2=[]170		for i in range(len(index2)):171			if image_path[n]==originimage[index2[i]]:172				myindex2.append(index2[i])173		myindex3=[]174		for i in range(len(index3)):175			if image_path[n]==originimage[index3[i]]:176				myindex3.append(index3[i])177		if len(myindex1)==0:178			continue179		if len(myindex2)==0:180			continue181		if len(myindex3)==0:182			continue183		myindex=myindex1+myindex2+myindex3184		original_img=cv2.imread(image_path[n], cv2.IMREAD_UNCHANGED)185		original_feature=predictor.img2feature(original_img)186		occluded_feature=deepcopy(original_feature)187		for i in range(len(myindex)):188			fhi=int(loc_set[myindex[i]][7])189			fwi=int(loc_set[myindex[i]][8])190			191			# Gause occlusion192			occluded_feature[0][fhi][fwi]=0193			occluded_feature[0][fhi-1][fwi]=0.25*occluded_feature[0][fhi-1][fwi]194			occluded_feature[0][fhi][fwi-1]=0.25*occluded_feature[0][fhi][fwi-1]195			occluded_feature[0][fhi+1][fwi]=0.25*occluded_feature[0][fhi+1][fwi]196			occluded_feature[0][fhi][fwi+1]=0.25*occluded_feature[0][fhi][fwi+1]197			occluded_feature[0][fhi-1][fwi-1]=0.375*occluded_feature[0][fhi-1][fwi-1]198			occluded_feature[0][fhi+1][fwi-1]=0.375*occluded_feature[0][fhi+1][fwi-1]199			occluded_feature[0][fhi+1][fwi+1]=0.375*occluded_feature[0][fhi+1][fwi+1]200			occluded_feature[0][fhi-1][fwi+1]=0.375*occluded_feature[0][fhi-1][fwi+1]201			202			occluded_feature[0][fhi+2][fwi]=0.625*occluded_feature[0][fhi+2][fwi]203			occluded_feature[0][fhi-2][fwi]=0.625*occluded_feature[0][fhi-2][fwi]204			occluded_feature[0][fhi][fwi-2]=0.625*occluded_feature[0][fhi][fwi-2]205			occluded_feature[0][fhi][fwi+2]=0.625*occluded_feature[0][fhi][fwi+2]206			occluded_feature[0][fhi-1][fwi-2]=0.75*occluded_feature[0][fhi-1][fwi-2]207			occluded_feature[0][fhi-1][fwi+2]=0.75*occluded_feature[0][fhi-1][fwi+2]208			occluded_feature[0][fhi+1][fwi-2]=0.75*occluded_feature[0][fhi+1][fwi-2]209			occluded_feature[0][fhi+1][fwi+2]=0.75*occluded_feature[0][fhi+1][fwi+2]210			occluded_feature[0][fhi-2][fwi-1]=0.75*occluded_feature[0][fhi-2][fwi-1]211			occluded_feature[0][fhi-2][fwi+1]=0.75*occluded_feature[0][fhi-2][fwi+1]212			occluded_feature[0][fhi+2][fwi-1]=0.75*occluded_feature[0][fhi+2][fwi-1]213			occluded_feature[0][fhi+2][fwi+1]=0.75*occluded_feature[0][fhi+2][fwi+1]214			occluded_feature[0][fhi-2][fwi-2]=0.875*occluded_feature[0][fhi-21][fwi-2]215			occluded_feature[0][fhi-2][fwi+2]=0.875*occluded_feature[0][fhi-2][fwi+2]216			occluded_feature[0][fhi+2][fwi-2]=0.875*occluded_feature[0][fhi+2][fwi-2]217			occluded_feature[0][fhi+2][fwi+2]=0.875*occluded_feature[0][fhi+2][fwi+2]				218			# print(hi)219			# print(wi)220			# print(patch_size)221		drop=GetPossDecrease(original_feature,occluded_feature,int(cat))222		if drop!=-1:223			myscore.append(drop)224	trip_img_vc_score.append(float(np.sum(myscore))/img_num)225np.savez(trip_save_file,vc_score=trip_img_vc_score,vc=trip_img_vc)226for s in quad_img_vc:227	dot1=s.find('_')228	k1=int(s[:dot1])229	s2=s[dot1+1:]230	dot2=s2.find('_')231	k2=int(s2[:dot2])232	s3=s2[dot2+1:]233	dot3=s3.find('_')234	k3=int(s3[:dot3])235	s4=s3[dot3+1:]236	k4=int(s4)237	target1=centers[k1]238	index1=np.where(assignment==k1)[0]239	index1=disttresh(index1,target1)240	target2=centers[k2]241	index2=np.where(assignment==k2)[0]242	index2=disttresh(index2,target2)243	target3=centers[k3]244	index3=np.where(assignment==k3)[0]245	index3=disttresh(index3,target3)246	target4=centers[k4]247	index4=np.where(assignment==k4)[0]248	index4=disttresh(index4,target4)249	myscore=[]250	for n in range(0,img_num):251		myindex1=[]252		for i in range(len(index1)):253			if image_path[n]==originimage[index1[i]]:254				myindex1.append(index1[i])255		#myindex=OnlyTheClosest(myindex,target), or other preprocessing method256		myindex2=[]257		for i in range(len(index2)):258			if image_path[n]==originimage[index2[i]]:259				myindex2.append(index2[i])260		myindex3=[]261		for i in range(len(index3)):262			if image_path[n]==originimage[index3[i]]:263				myindex3.append(index3[i])264		myindex4=[]265		for i in range(len(index4)):266			if image_path[n]==originimage[index4[i]]:267				myindex4.append(index4[i])268		if len(myindex1)==0:269			continue270		if len(myindex2)==0:271			continue272		if len(myindex3)==0:273			continue274		if len(myindex4)==0:275			continue276		myindex=myindex1+myindex2+myindex3+myindex4277		original_img=cv2.imread(image_path[n], cv2.IMREAD_UNCHANGED)278		original_feature=predictor.img2feature(original_img)279		occluded_feature=deepcopy(original_feature)280		for i in range(len(myindex)):281			fhi=int(loc_set[myindex[i]][7])282			fwi=int(loc_set[myindex[i]][8])283			284			# Gause occlusion285			occluded_feature[0][fhi][fwi]=0286			occluded_feature[0][fhi-1][fwi]=0.25*occluded_feature[0][fhi-1][fwi]287			occluded_feature[0][fhi][fwi-1]=0.25*occluded_feature[0][fhi][fwi-1]288			occluded_feature[0][fhi+1][fwi]=0.25*occluded_feature[0][fhi+1][fwi]289			occluded_feature[0][fhi][fwi+1]=0.25*occluded_feature[0][fhi][fwi+1]290			occluded_feature[0][fhi-1][fwi-1]=0.375*occluded_feature[0][fhi-1][fwi-1]291			occluded_feature[0][fhi+1][fwi-1]=0.375*occluded_feature[0][fhi+1][fwi-1]292			occluded_feature[0][fhi+1][fwi+1]=0.375*occluded_feature[0][fhi+1][fwi+1]293			occluded_feature[0][fhi-1][fwi+1]=0.375*occluded_feature[0][fhi-1][fwi+1]294			295			occluded_feature[0][fhi+2][fwi]=0.625*occluded_feature[0][fhi+2][fwi]296			occluded_feature[0][fhi-2][fwi]=0.625*occluded_feature[0][fhi-2][fwi]297			occluded_feature[0][fhi][fwi-2]=0.625*occluded_feature[0][fhi][fwi-2]298			occluded_feature[0][fhi][fwi+2]=0.625*occluded_feature[0][fhi][fwi+2]299			occluded_feature[0][fhi-1][fwi-2]=0.75*occluded_feature[0][fhi-1][fwi-2]300			occluded_feature[0][fhi-1][fwi+2]=0.75*occluded_feature[0][fhi-1][fwi+2]301			occluded_feature[0][fhi+1][fwi-2]=0.75*occluded_feature[0][fhi+1][fwi-2]302			occluded_feature[0][fhi+1][fwi+2]=0.75*occluded_feature[0][fhi+1][fwi+2]303			occluded_feature[0][fhi-2][fwi-1]=0.75*occluded_feature[0][fhi-2][fwi-1]304			occluded_feature[0][fhi-2][fwi+1]=0.75*occluded_feature[0][fhi-2][fwi+1]305			occluded_feature[0][fhi+2][fwi-1]=0.75*occluded_feature[0][fhi+2][fwi-1]306			occluded_feature[0][fhi+2][fwi+1]=0.75*occluded_feature[0][fhi+2][fwi+1]307			occluded_feature[0][fhi-2][fwi-2]=0.875*occluded_feature[0][fhi-21][fwi-2]308			occluded_feature[0][fhi-2][fwi+2]=0.875*occluded_feature[0][fhi-2][fwi+2]309			occluded_feature[0][fhi+2][fwi-2]=0.875*occluded_feature[0][fhi+2][fwi-2]310			occluded_feature[0][fhi+2][fwi+2]=0.875*occluded_feature[0][fhi+2][fwi+2]				311			# print(hi)312			# print(wi)313			# print(patch_size)314		drop=GetPossDecrease(original_feature,occluded_feature,int(cat))315		if drop!=-1:316			myscore.append(drop)317	quad_img_vc_score.append(float(np.sum(myscore))/img_num)...

DragFeature.js

Source:DragFeature.js

...136                }, this.dragCallbacks), {137                    documentDrag: this.documentDrag138                }139            ),140            feature: new OpenLayers.Handler.Feature(141                this, this.layer, OpenLayers.Util.extend({142                    // 'click' and 'clickout' callback are for the mobile143                    // support: no 'over' or 'out' in touch based browsers.144                    click: this.clickFeature,145                    clickout: this.clickoutFeature,146                    over: this.overFeature,147                    out: this.outFeature148                }, this.featureCallbacks),149                {geometryTypes: this.geometryTypes}150            )151        };152    },153    /**154     * Method: clickFeature155     * Called when the feature handler detects a click-in on a feature.156     *157     * Parameters:158     * feature - {<OpenLayers.Feature.Vector>}159     */160    clickFeature: function(feature) {161        if (this.handlers.feature.touch && !this.over && this.overFeature(feature)) {162            this.handlers.drag.dragstart(this.handlers.feature.evt);163            // to let the events propagate to the feature handler (click callback)164            this.handlers.drag.stopDown = false;165        }166    },167    /**168     * Method: clickoutFeature169     * Called when the feature handler detects a click-out on a feature.170     *171     * Parameters:172     * feature - {<OpenLayers.Feature.Vector>}173     */174    clickoutFeature: function(feature) {175        if (this.handlers.feature.touch && this.over) {176            this.outFeature(feature);177            this.handlers.drag.stopDown = true;178        }179    },180    /**181     * APIMethod: destroy182     * Take care of things that are not handled in superclass183     */184    destroy: function() {185        this.layer = null;186        OpenLayers.Control.prototype.destroy.apply(this, []);187    },188    /**189     * APIMethod: activate190     * Activate the control and the feature handler.191     * 192     * Returns:193     * {Boolean} Successfully activated the control and feature handler.194     */195    activate: function() {196        return (this.handlers.feature.activate() &&197                OpenLayers.Control.prototype.activate.apply(this, arguments));198    },199    /**200     * APIMethod: deactivate201     * Deactivate the control and all handlers.202     * 203     * Returns:204     * {Boolean} Successfully deactivated the control.205     */206    deactivate: function() {207        // the return from the handlers is unimportant in this case208        this.handlers.drag.deactivate();209        this.handlers.feature.deactivate();210        this.feature = null;211        this.dragging = false;212        this.lastPixel = null;213        OpenLayers.Element.removeClass(214            this.map.viewPortDiv, this.displayClass + "Over"215        );216        return OpenLayers.Control.prototype.deactivate.apply(this, arguments);217    },218    /**219     * Method: overFeature220     * Called when the feature handler detects a mouse-over on a feature.221     *     This activates the drag handler.222     *223     * Parameters:224     * feature - {<OpenLayers.Feature.Vector>} The selected feature.225     *226     * Returns:227     * {Boolean} Successfully activated the drag handler.228     */229    overFeature: function(feature) {230        var activated = false;231        if(!this.handlers.drag.dragging) {232            this.feature = feature;233            this.handlers.drag.activate();234            activated = true;235            this.over = true;236            OpenLayers.Element.addClass(this.map.viewPortDiv, this.displayClass + "Over");237            this.onEnter(feature);238        } else {239            if(this.feature.id == feature.id) {240                this.over = true;241            } else {242                this.over = false;243            }244        }245        return activated;246    },247    /**248     * Method: downFeature249     * Called when the drag handler detects a mouse-down.250     *251     * Parameters:252     * pixel - {<OpenLayers.Pixel>} Location of the mouse event.253     */254    downFeature: function(pixel) {255        this.lastPixel = pixel;256        this.onStart(this.feature, pixel);257    },258    /**259     * Method: moveFeature260     * Called when the drag handler detects a mouse-move.  Also calls the261     *     optional onDrag method.262     * 263     * Parameters:264     * pixel - {<OpenLayers.Pixel>} Location of the mouse event.265     */266    moveFeature: function(pixel) {267        var res = this.map.getResolution();268        this.feature.geometry.move(res * (pixel.x - this.lastPixel.x),269                                   res * (this.lastPixel.y - pixel.y));270        this.layer.drawFeature(this.feature);271        this.lastPixel = pixel;272        this.onDrag(this.feature, pixel);273    },274    /**275     * Method: upFeature276     * Called when the drag handler detects a mouse-up.277     * 278     * Parameters:279     * pixel - {<OpenLayers.Pixel>} Location of the mouse event.280     */281    upFeature: function(pixel) {282        if(!this.over) {283            this.handlers.drag.deactivate();284        }...

ToolboxView.js

Source:ToolboxView.js

...46                        var Feature = featureManager.get(featureName);47                        if (!Feature) {48                            return;49                        }50                        feature = new Feature(featureModel);51                    }52                    features[featureName] = feature;53                }54                else {55                    feature = features[oldName];56                    // If feature does not exsit.57                    if (!feature) {58                        return;59                    }60                    feature.model = featureModel;61                }62                if (!featureName && oldName) {63                    feature.dispose && feature.dispose(ecModel, api);64                    return;...

Using AI Code Generation

1var f = new root.Feature();2var f = new root.Feature();3var f = new root.Feature();4var f = new root.Feature();5var f = new root.Feature();6var f = new root.Feature();7var f = new root.Feature();8var f = new root.Feature();9var f = new root.Feature();10var f = new root.Feature();11var f = new root.Feature();12var f = new root.Feature();13var f = new root.Feature();14var f = new root.Feature();15var f = new root.Feature();

Using AI Code Generation

1var root = require('root');2root.Feature('test', function() {3    this.Given(/^I am on the homepage$/, function(callback) {4        callback.pending();5    });6    this.When(/^I search for "([^"]*)"$/, function(arg1, callback) {7        callback.pending();8    });9    this.Then(/^I should see "([^"]*)"$/, function(arg1, callback) {10        callback.pending();11    });12});13root.Feature('test', function() {14    this.Given(/^I am on the homepage$/, function(callback) {15        callback.pending();16    });17    this.When(/^I search for "([^"]*)"$/, function(arg1, callback) {18        callback.pending();19    });20    this.Then(/^I should see "([^"]*)"$/, function(arg1, callback) {21        callback.pending();22    });23});24var root = require('root');25root.Feature('test', function() {26    this.Given(/^I am on the homepage$/, function(callback) {27        callback.pending();28    });29    this.When(/^I search for "([^"]*)"$/, function(arg1, callback) {30        callback.pending();31    });32    this.Then(/^I should see "([^"]*)"$/, function(arg1, callback) {33        callback.pending();34    });35});36var root = require('root');37root.Feature('test', function() {38    this.Given(/^I am on the homepage$/, function(callback) {39        callback.pending();40    });41    this.When(/^I search for "([^"]*)"$/, function(arg1, callback) {42        callback.pending();43    });44    this.Then(/^I should see "([^"]*)"$/,

Using AI Code Generation

1var feature = require('feature');2feature.Feature("test", function() {3});4var feature = require('feature');5feature.Feature("test", function() {6});7var feature = require('feature');8feature.Feature("test", function() {9});

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