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How to use classification method in assertpy

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losses_builder_test.py

Source:losses_builder_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 losses_builder."""16import tensorflow as tf17from google.protobuf import text_format18from object_detection.builders import losses_builder19from object_detection.core import losses20from object_detection.protos import losses_pb221from object_detection.utils import ops22class LocalizationLossBuilderTest(tf.test.TestCase):23  def test_build_weighted_l2_localization_loss(self):24    losses_text_proto = """25      localization_loss {26        weighted_l2 {27        }28      }29      classification_loss {30        weighted_softmax {31        }32      }33    """34    losses_proto = losses_pb2.Loss()35    text_format.Merge(losses_text_proto, losses_proto)36    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)37    self.assertTrue(isinstance(localization_loss,38                               losses.WeightedL2LocalizationLoss))39  def test_build_weighted_smooth_l1_localization_loss_default_delta(self):40    losses_text_proto = """41      localization_loss {42        weighted_smooth_l1 {43        }44      }45      classification_loss {46        weighted_softmax {47        }48      }49    """50    losses_proto = losses_pb2.Loss()51    text_format.Merge(losses_text_proto, losses_proto)52    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)53    self.assertTrue(isinstance(localization_loss,54                               losses.WeightedSmoothL1LocalizationLoss))55    self.assertAlmostEqual(localization_loss._delta, 1.0)56  def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):57    losses_text_proto = """58      localization_loss {59        weighted_smooth_l1 {60          delta: 0.161        }62      }63      classification_loss {64        weighted_softmax {65        }66      }67    """68    losses_proto = losses_pb2.Loss()69    text_format.Merge(losses_text_proto, losses_proto)70    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)71    self.assertTrue(isinstance(localization_loss,72                               losses.WeightedSmoothL1LocalizationLoss))73    self.assertAlmostEqual(localization_loss._delta, 0.1)74  def test_build_weighted_iou_localization_loss(self):75    losses_text_proto = """76      localization_loss {77        weighted_iou {78        }79      }80      classification_loss {81        weighted_softmax {82        }83      }84    """85    losses_proto = losses_pb2.Loss()86    text_format.Merge(losses_text_proto, losses_proto)87    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)88    self.assertTrue(isinstance(localization_loss,89                               losses.WeightedIOULocalizationLoss))90  def test_anchorwise_output(self):91    losses_text_proto = """92      localization_loss {93        weighted_smooth_l1 {94        }95      }96      classification_loss {97        weighted_softmax {98        }99      }100    """101    losses_proto = losses_pb2.Loss()102    text_format.Merge(losses_text_proto, losses_proto)103    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)104    self.assertTrue(isinstance(localization_loss,105                               losses.WeightedSmoothL1LocalizationLoss))106    predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])107    targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])108    weights = tf.constant([[1.0, 1.0]])109    loss = localization_loss(predictions, targets, weights=weights)110    self.assertEqual(loss.shape, [1, 2])111  def test_raise_error_on_empty_localization_config(self):112    losses_text_proto = """113      classification_loss {114        weighted_softmax {115        }116      }117    """118    losses_proto = losses_pb2.Loss()119    text_format.Merge(losses_text_proto, losses_proto)120    with self.assertRaises(ValueError):121      losses_builder._build_localization_loss(losses_proto)122class ClassificationLossBuilderTest(tf.test.TestCase):123  def test_build_weighted_sigmoid_classification_loss(self):124    losses_text_proto = """125      classification_loss {126        weighted_sigmoid {127        }128      }129      localization_loss {130        weighted_l2 {131        }132      }133    """134    losses_proto = losses_pb2.Loss()135    text_format.Merge(losses_text_proto, losses_proto)136    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)137    self.assertTrue(isinstance(classification_loss,138                               losses.WeightedSigmoidClassificationLoss))139  def test_build_weighted_sigmoid_focal_classification_loss(self):140    losses_text_proto = """141      classification_loss {142        weighted_sigmoid_focal {143        }144      }145      localization_loss {146        weighted_l2 {147        }148      }149    """150    losses_proto = losses_pb2.Loss()151    text_format.Merge(losses_text_proto, losses_proto)152    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)153    self.assertTrue(isinstance(classification_loss,154                               losses.SigmoidFocalClassificationLoss))155    self.assertAlmostEqual(classification_loss._alpha, None)156    self.assertAlmostEqual(classification_loss._gamma, 2.0)157  def test_build_weighted_sigmoid_focal_loss_non_default(self):158    losses_text_proto = """159      classification_loss {160        weighted_sigmoid_focal {161          alpha: 0.25162          gamma: 3.0163        }164      }165      localization_loss {166        weighted_l2 {167        }168      }169    """170    losses_proto = losses_pb2.Loss()171    text_format.Merge(losses_text_proto, losses_proto)172    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)173    self.assertTrue(isinstance(classification_loss,174                               losses.SigmoidFocalClassificationLoss))175    self.assertAlmostEqual(classification_loss._alpha, 0.25)176    self.assertAlmostEqual(classification_loss._gamma, 3.0)177  def test_build_weighted_softmax_classification_loss(self):178    losses_text_proto = """179      classification_loss {180        weighted_softmax {181        }182      }183      localization_loss {184        weighted_l2 {185        }186      }187    """188    losses_proto = losses_pb2.Loss()189    text_format.Merge(losses_text_proto, losses_proto)190    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)191    self.assertTrue(isinstance(classification_loss,192                               losses.WeightedSoftmaxClassificationLoss))193  def test_build_weighted_logits_softmax_classification_loss(self):194    losses_text_proto = """195      classification_loss {196        weighted_logits_softmax {197        }198      }199      localization_loss {200        weighted_l2 {201        }202      }203    """204    losses_proto = losses_pb2.Loss()205    text_format.Merge(losses_text_proto, losses_proto)206    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)207    self.assertTrue(208        isinstance(classification_loss,209                   losses.WeightedSoftmaxClassificationAgainstLogitsLoss))210  def test_build_weighted_softmax_classification_loss_with_logit_scale(self):211    losses_text_proto = """212      classification_loss {213        weighted_softmax {214          logit_scale: 2.0215        }216      }217      localization_loss {218        weighted_l2 {219        }220      }221    """222    losses_proto = losses_pb2.Loss()223    text_format.Merge(losses_text_proto, losses_proto)224    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)225    self.assertTrue(isinstance(classification_loss,226                               losses.WeightedSoftmaxClassificationLoss))227  def test_build_bootstrapped_sigmoid_classification_loss(self):228    losses_text_proto = """229      classification_loss {230        bootstrapped_sigmoid {231          alpha: 0.5232        }233      }234      localization_loss {235        weighted_l2 {236        }237      }238    """239    losses_proto = losses_pb2.Loss()240    text_format.Merge(losses_text_proto, losses_proto)241    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)242    self.assertTrue(isinstance(classification_loss,243                               losses.BootstrappedSigmoidClassificationLoss))244  def test_anchorwise_output(self):245    losses_text_proto = """246      classification_loss {247        weighted_sigmoid {248          anchorwise_output: true249        }250      }251      localization_loss {252        weighted_l2 {253        }254      }255    """256    losses_proto = losses_pb2.Loss()257    text_format.Merge(losses_text_proto, losses_proto)258    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)259    self.assertTrue(isinstance(classification_loss,260                               losses.WeightedSigmoidClassificationLoss))261    predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])262    targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])263    weights = tf.constant([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]])264    loss = classification_loss(predictions, targets, weights=weights)265    self.assertEqual(loss.shape, [1, 2, 3])266  def test_raise_error_on_empty_config(self):267    losses_text_proto = """268      localization_loss {269        weighted_l2 {270        }271      }272    """273    losses_proto = losses_pb2.Loss()274    text_format.Merge(losses_text_proto, losses_proto)275    with self.assertRaises(ValueError):276      losses_builder.build(losses_proto)277class HardExampleMinerBuilderTest(tf.test.TestCase):278  def test_do_not_build_hard_example_miner_by_default(self):279    losses_text_proto = """280      localization_loss {281        weighted_l2 {282        }283      }284      classification_loss {285        weighted_softmax {286        }287      }288    """289    losses_proto = losses_pb2.Loss()290    text_format.Merge(losses_text_proto, losses_proto)291    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)292    self.assertEqual(hard_example_miner, None)293  def test_build_hard_example_miner_for_classification_loss(self):294    losses_text_proto = """295      localization_loss {296        weighted_l2 {297        }298      }299      classification_loss {300        weighted_softmax {301        }302      }303      hard_example_miner {304        loss_type: CLASSIFICATION305      }306    """307    losses_proto = losses_pb2.Loss()308    text_format.Merge(losses_text_proto, losses_proto)309    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)310    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))311    self.assertEqual(hard_example_miner._loss_type, 'cls')312  def test_build_hard_example_miner_for_localization_loss(self):313    losses_text_proto = """314      localization_loss {315        weighted_l2 {316        }317      }318      classification_loss {319        weighted_softmax {320        }321      }322      hard_example_miner {323        loss_type: LOCALIZATION324      }325    """326    losses_proto = losses_pb2.Loss()327    text_format.Merge(losses_text_proto, losses_proto)328    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)329    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))330    self.assertEqual(hard_example_miner._loss_type, 'loc')331  def test_build_hard_example_miner_with_non_default_values(self):332    losses_text_proto = """333      localization_loss {334        weighted_l2 {335        }336      }337      classification_loss {338        weighted_softmax {339        }340      }341      hard_example_miner {342        num_hard_examples: 32343        iou_threshold: 0.5344        loss_type: LOCALIZATION345        max_negatives_per_positive: 10346        min_negatives_per_image: 3347      }348    """349    losses_proto = losses_pb2.Loss()350    text_format.Merge(losses_text_proto, losses_proto)351    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)352    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))353    self.assertEqual(hard_example_miner._num_hard_examples, 32)354    self.assertAlmostEqual(hard_example_miner._iou_threshold, 0.5)355    self.assertEqual(hard_example_miner._max_negatives_per_positive, 10)356    self.assertEqual(hard_example_miner._min_negatives_per_image, 3)357class LossBuilderTest(tf.test.TestCase):358  def test_build_all_loss_parameters(self):359    losses_text_proto = """360      localization_loss {361        weighted_l2 {362        }363      }364      classification_loss {365        weighted_softmax {366        }367      }368      hard_example_miner {369      }370      classification_weight: 0.8371      localization_weight: 0.2372    """373    losses_proto = losses_pb2.Loss()374    text_format.Merge(losses_text_proto, losses_proto)375    (classification_loss, localization_loss, classification_weight,376     localization_weight, hard_example_miner, _,377     _) = losses_builder.build(losses_proto)378    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))379    self.assertTrue(isinstance(classification_loss,380                               losses.WeightedSoftmaxClassificationLoss))381    self.assertTrue(isinstance(localization_loss,382                               losses.WeightedL2LocalizationLoss))383    self.assertAlmostEqual(classification_weight, 0.8)384    self.assertAlmostEqual(localization_weight, 0.2)385  def test_build_expected_sampling(self):386    losses_text_proto = """387      localization_loss {388        weighted_l2 {389        }390      }391      classification_loss {392        weighted_softmax {393        }394      }395      hard_example_miner {396      }397      classification_weight: 0.8398      localization_weight: 0.2399    """400    losses_proto = losses_pb2.Loss()401    text_format.Merge(losses_text_proto, losses_proto)402    (classification_loss, localization_loss, classification_weight,403     localization_weight, hard_example_miner, _,404     _) = losses_builder.build(losses_proto)405    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))406    self.assertTrue(407        isinstance(classification_loss,408                   losses.WeightedSoftmaxClassificationLoss))409    self.assertTrue(410        isinstance(localization_loss, losses.WeightedL2LocalizationLoss))411    self.assertAlmostEqual(classification_weight, 0.8)412    self.assertAlmostEqual(localization_weight, 0.2)413  def test_build_reweighting_unmatched_anchors(self):414    losses_text_proto = """415      localization_loss {416        weighted_l2 {417        }418      }419      classification_loss {420        weighted_softmax {421        }422      }423      hard_example_miner {424      }425      classification_weight: 0.8426      localization_weight: 0.2427    """428    losses_proto = losses_pb2.Loss()429    text_format.Merge(losses_text_proto, losses_proto)430    (classification_loss, localization_loss, classification_weight,431     localization_weight, hard_example_miner, _,432     _) = losses_builder.build(losses_proto)433    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))434    self.assertTrue(435        isinstance(classification_loss,436                   losses.WeightedSoftmaxClassificationLoss))437    self.assertTrue(438        isinstance(localization_loss, losses.WeightedL2LocalizationLoss))439    self.assertAlmostEqual(classification_weight, 0.8)440    self.assertAlmostEqual(localization_weight, 0.2)441  def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):442    losses_text_proto = """443      localization_loss {444        weighted_l2 {445        }446      }447      classification_loss {448        weighted_sigmoid_focal {449        }450      }451      hard_example_miner {452      }453      classification_weight: 0.8454      localization_weight: 0.2455    """456    losses_proto = losses_pb2.Loss()457    text_format.Merge(losses_text_proto, losses_proto)458    with self.assertRaises(ValueError):459      losses_builder.build(losses_proto)460class FasterRcnnClassificationLossBuilderTest(tf.test.TestCase):461  def test_build_sigmoid_loss(self):462    losses_text_proto = """463      weighted_sigmoid {464      }465    """466    losses_proto = losses_pb2.ClassificationLoss()467    text_format.Merge(losses_text_proto, losses_proto)468    classification_loss = losses_builder.build_faster_rcnn_classification_loss(469        losses_proto)470    self.assertTrue(isinstance(classification_loss,471                               losses.WeightedSigmoidClassificationLoss))472  def test_build_softmax_loss(self):473    losses_text_proto = """474      weighted_softmax {475      }476    """477    losses_proto = losses_pb2.ClassificationLoss()478    text_format.Merge(losses_text_proto, losses_proto)479    classification_loss = losses_builder.build_faster_rcnn_classification_loss(480        losses_proto)481    self.assertTrue(isinstance(classification_loss,482                               losses.WeightedSoftmaxClassificationLoss))483  def test_build_logits_softmax_loss(self):484    losses_text_proto = """485      weighted_logits_softmax {486      }487    """488    losses_proto = losses_pb2.ClassificationLoss()489    text_format.Merge(losses_text_proto, losses_proto)490    classification_loss = losses_builder.build_faster_rcnn_classification_loss(491        losses_proto)492    self.assertTrue(493        isinstance(classification_loss,494                   losses.WeightedSoftmaxClassificationAgainstLogitsLoss))495  def test_build_sigmoid_focal_loss(self):496    losses_text_proto = """497      weighted_sigmoid_focal {498      }499    """500    losses_proto = losses_pb2.ClassificationLoss()501    text_format.Merge(losses_text_proto, losses_proto)502    classification_loss = losses_builder.build_faster_rcnn_classification_loss(503        losses_proto)504    self.assertTrue(505        isinstance(classification_loss,506                   losses.SigmoidFocalClassificationLoss))507  def test_build_softmax_loss_by_default(self):508    losses_text_proto = """509    """510    losses_proto = losses_pb2.ClassificationLoss()511    text_format.Merge(losses_text_proto, losses_proto)512    classification_loss = losses_builder.build_faster_rcnn_classification_loss(513        losses_proto)514    self.assertTrue(isinstance(classification_loss,515                               losses.WeightedSoftmaxClassificationLoss))516if __name__ == '__main__':...

config.py

Source:config.py

1from constants import *2CLASSIFICATIONS = {3    "catvsDog": {4        "img_size"   : 224,5        "normalize"  : True,6    },7    "faceMaskClassification": {8        "img_size"   : 224,9        "normalize"  : True,10    },11    "genderClassification": {12        "img_size"   : 150,13        "normalize"  : True,14    },15    "emotionClassification": {16        "img_size"   : 48,17        "normalize"  : True,18    },19    "glassesClassification":{20        "img_size"   : 160,21        "normalize"  : False22    },23    "ageClassification": {24        "img_size"   : 160,25        "normalize"  : True,26    },27    "foodClassification":{28        "img_size"   : 224,29        "normalize"  : True,30    },31    "dogClassification": {32        "img_size"   : 224,33        "normalize"  : True,34    },35    "birdsClassification": {36        "img_size"   : 224,37        "normalize"  : True,38    },39    "wildlifeClassification": {40        "img_size"   : 224,41        "normalize"  : False, 42    },43    "landmarkClassification": {44        "img_size"   : 224,45        "normalize"  : True,46    },47    "flowerClassification": {48        "img_size"   : 224,49        "normalize"  : True,50    },51    "animalClassification": {52        "img_size"   : 160,53        "normalize"  : True,54    },55    "sportsClassification": {56        "img_size"   : 224,57        "normalize"  : True,58    },59}60MODEL_LOCATIONS = {61    "catvsDog"               : "./models_quantized/catVsDogMobilenetv224_quantized.tflite",62    "faceMaskClassification" : "./models_quantized/faceMaskClassificationNasNetModel224_quantized.tflite",63    "genderClassification"   : "./models_quantized/GenderModal_quantized.tflite",64    "emotionClassification"  : "./models_quantized/emotionDetection_quantized.tflite",65    "glassesClassification"  : "./models_quantized/glassesDetection_quantized.tflite",66    "ageClassification"      : "./models_quantized/ageClassificationNasNetModel160_quantized.tflite",67    "foodClassification"     : "./models_quantized/foodClassificationMobilenetv2_quantized.tflite",68    "dogClassification"      : "./models_quantized/dogClassificationMobileNet224(78)_quantized.tflite",69    "birdsClassification"    : "./models_quantized/birdClassificationNasNetModel224_quantized.tflite",70    "wildlifeClassification" : "./models_quantized/oregonWildlife224(92)_quantized.tflite",71    "landmarkClassification" : "./models_quantized/landMarks224_quantized.tflite",72    "flowerClassification"   : "./models_quantized/flower299MobileNet244(76)_quantized.tflite",73    "animalClassification"   : "./models_quantized/animalClassification160_quantized.tflite",74    "sportsClassification"   : "./models_quantized/sportsClassification224_quantized.tflite",75}76LABELS = {77    "catvsDog"               : None,78    "faceMaskClassification" : None,79    "genderClassification"   : None,80    "emotionClassification"  : None,81    "glassesClassification"  : None,82    "ageClassification"      : None,83    "foodClassification"     : food_labels,84    "dogClassification"      : dog_breeds_labels,85    "birdsClassification"    : birds_names_labels,86    "wildlifeClassification" : wildlife_labels,87    "landmarkClassification" : landmark_labels,88    "flowerClassification"   : flowers_name_labels,89    "animalClassification"   : animal_labels,90    "sportsClassification"   : sports_labels,91}92MULTIPLE_CLASSES = {93    "catvsDog"               : False,94    "faceMaskClassification" : False,95    "genderClassification"   : False,96    "emotionClassification"  : False,97    "glassesClassification"  : False,98    "ageClassification"      : False,99    "foodClassification"     : True,100    "dogClassification"      : True,101    "birdsClassification"    : True,102    "wildlifeClassification" : True,103    "landmarkClassification" : True,104    "flowerClassification"   : True,105    "animalClassification"   : True,106    "sportsClassification"   : True,...

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