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"""Tests for object_detection.predictors.heads.mask_head.""" |
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import tensorflow as tf |
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from google.protobuf import text_format |
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from object_detection.builders import hyperparams_builder |
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from object_detection.predictors.heads import keras_mask_head |
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from object_detection.protos import hyperparams_pb2 |
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from object_detection.utils import test_case |
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class ConvolutionalMaskPredictorTest(test_case.TestCase): |
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def _build_conv_hyperparams(self): |
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conv_hyperparams = hyperparams_pb2.Hyperparams() |
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conv_hyperparams_text_proto = """ |
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activation: NONE |
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regularizer { |
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l2_regularizer { |
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} |
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} |
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initializer { |
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truncated_normal_initializer { |
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} |
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} |
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""" |
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text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams) |
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return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams) |
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def test_prediction_size_use_depthwise_false(self): |
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conv_hyperparams = self._build_conv_hyperparams() |
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mask_prediction_head = keras_mask_head.ConvolutionalMaskHead( |
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is_training=True, |
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num_classes=20, |
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use_dropout=True, |
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dropout_keep_prob=0.5, |
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kernel_size=3, |
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conv_hyperparams=conv_hyperparams, |
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freeze_batchnorm=False, |
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num_predictions_per_location=1, |
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use_depthwise=False, |
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mask_height=7, |
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mask_width=7) |
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image_feature = tf.random_uniform( |
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[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32) |
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mask_predictions = mask_prediction_head(image_feature) |
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self.assertAllEqual([64, 323, 20, 7, 7], |
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mask_predictions.get_shape().as_list()) |
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def test_prediction_size_use_depthwise_true(self): |
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conv_hyperparams = self._build_conv_hyperparams() |
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mask_prediction_head = keras_mask_head.ConvolutionalMaskHead( |
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is_training=True, |
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num_classes=20, |
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use_dropout=True, |
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dropout_keep_prob=0.5, |
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kernel_size=3, |
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conv_hyperparams=conv_hyperparams, |
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freeze_batchnorm=False, |
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num_predictions_per_location=1, |
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use_depthwise=True, |
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mask_height=7, |
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mask_width=7) |
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image_feature = tf.random_uniform( |
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[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32) |
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mask_predictions = mask_prediction_head(image_feature) |
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self.assertAllEqual([64, 323, 20, 7, 7], |
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mask_predictions.get_shape().as_list()) |
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def test_class_agnostic_prediction_size_use_depthwise_false(self): |
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conv_hyperparams = self._build_conv_hyperparams() |
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mask_prediction_head = keras_mask_head.ConvolutionalMaskHead( |
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is_training=True, |
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num_classes=20, |
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use_dropout=True, |
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dropout_keep_prob=0.5, |
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kernel_size=3, |
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conv_hyperparams=conv_hyperparams, |
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freeze_batchnorm=False, |
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num_predictions_per_location=1, |
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use_depthwise=False, |
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mask_height=7, |
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mask_width=7, |
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masks_are_class_agnostic=True) |
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image_feature = tf.random_uniform( |
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[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32) |
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mask_predictions = mask_prediction_head(image_feature) |
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self.assertAllEqual([64, 323, 1, 7, 7], |
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mask_predictions.get_shape().as_list()) |
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def test_class_agnostic_prediction_size_use_depthwise_true(self): |
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conv_hyperparams = self._build_conv_hyperparams() |
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mask_prediction_head = keras_mask_head.ConvolutionalMaskHead( |
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is_training=True, |
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num_classes=20, |
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use_dropout=True, |
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dropout_keep_prob=0.5, |
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kernel_size=3, |
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conv_hyperparams=conv_hyperparams, |
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freeze_batchnorm=False, |
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num_predictions_per_location=1, |
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use_depthwise=True, |
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mask_height=7, |
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mask_width=7, |
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masks_are_class_agnostic=True) |
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image_feature = tf.random_uniform( |
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[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32) |
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mask_predictions = mask_prediction_head(image_feature) |
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self.assertAllEqual([64, 323, 1, 7, 7], |
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mask_predictions.get_shape().as_list()) |
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if __name__ == '__main__': |
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tf.test.main() |
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