def test_build_box_predictor_with_mask_branch(self):
   box_predictor_proto = box_predictor_pb2.BoxPredictor()
   box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
       hyperparams_pb2.Hyperparams.FC)
   box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams.op = (
       hyperparams_pb2.Hyperparams.CONV)
   box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
   box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
   mock_argscope_fn = mock.Mock(return_value='arg_scope')
   box_predictor = box_predictor_builder.build(
       argscope_fn=mock_argscope_fn,
       box_predictor_config=box_predictor_proto,
       is_training=True,
       num_classes=90)
   mock_argscope_fn.assert_has_calls(
       [mock.call(box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams,
                  True),
        mock.call(box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams,
                  True)], any_order=True)
   self.assertFalse(box_predictor._use_dropout)
   self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.5)
   self.assertEqual(box_predictor.num_classes, 90)
   self.assertTrue(box_predictor._is_training)
   self.assertEqual(box_predictor._box_code_size, 4)
   self.assertTrue(box_predictor._predict_instance_masks)
   self.assertEqual(box_predictor._mask_prediction_conv_depth, 512)
   self.assertFalse(box_predictor._predict_keypoints)
  def test_default_rfcn_box_predictor(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 4)
    self.assertEqual(box_predictor._num_spatial_bins, [3, 3])
    self.assertEqual(box_predictor._crop_size, [12, 12])
 def test_box_predictor_builder_calls_fc_argscope_fn(self):
   fc_hyperparams_text_proto = """
     regularizer {
       l1_regularizer {
         weight: 0.0003
       }
     }
     initializer {
       truncated_normal_initializer {
         mean: 0.0
         stddev: 0.3
       }
     }
     activation: RELU_6
     op: FC
   """
   hyperparams_proto = hyperparams_pb2.Hyperparams()
   text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
   box_predictor_proto = box_predictor_pb2.BoxPredictor()
   box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
       hyperparams_proto)
   mock_argscope_fn = mock.Mock(return_value='arg_scope')
   box_predictor = box_predictor_builder.build(
       argscope_fn=mock_argscope_fn,
       box_predictor_config=box_predictor_proto,
       is_training=False,
       num_classes=10)
   mock_argscope_fn.assert_called_with(hyperparams_proto, False)
   self.assertEqual(box_predictor._fc_hyperparams, 'arg_scope')
Ejemplo n.º 4
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    def _get_second_stage_box_predictor(self,
                                        num_classes,
                                        is_training,
                                        predict_masks,
                                        masks_are_class_agnostic,
                                        share_box_across_classes=False,
                                        use_keras=False):
        box_predictor_proto = box_predictor_pb2.BoxPredictor()
        text_format.Merge(
            self._get_second_stage_box_predictor_text_proto(
                share_box_across_classes), box_predictor_proto)
        if predict_masks:
            text_format.Merge(
                self._add_mask_to_second_stage_box_predictor_text_proto(
                    masks_are_class_agnostic), box_predictor_proto)

        if use_keras:
            return box_predictor_builder.build_keras(
                hyperparams_builder.KerasLayerHyperparams,
                inplace_batchnorm_update=False,
                freeze_batchnorm=False,
                box_predictor_config=box_predictor_proto,
                num_classes=num_classes,
                num_predictions_per_location_list=None,
                is_training=is_training)
        else:
            return box_predictor_builder.build(hyperparams_builder.build,
                                               box_predictor_proto,
                                               num_classes=num_classes,
                                               is_training=is_training)
 def test_construct_default_conv_box_predictor(self):
   box_predictor_text_proto = """
     convolutional_box_predictor {
       conv_hyperparams {
         regularizer {
           l1_regularizer {
           }
         }
         initializer {
           truncated_normal_initializer {
           }
         }
       }
     }"""
   box_predictor_proto = box_predictor_pb2.BoxPredictor()
   text_format.Merge(box_predictor_text_proto, box_predictor_proto)
   box_predictor = box_predictor_builder.build(
       argscope_fn=hyperparams_builder.build,
       box_predictor_config=box_predictor_proto,
       is_training=True,
       num_classes=90)
   self.assertEqual(box_predictor._min_depth, 0)
   self.assertEqual(box_predictor._max_depth, 0)
   self.assertEqual(box_predictor._num_layers_before_predictor, 0)
   self.assertTrue(box_predictor._use_dropout)
   self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.8)
   self.assertFalse(box_predictor._apply_sigmoid_to_scores)
   self.assertEqual(box_predictor.num_classes, 90)
   self.assertTrue(box_predictor._is_training)
Ejemplo n.º 6
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 def test_construct_default_conv_box_predictor_with_batch_norm(self):
     box_predictor_text_proto = """
   weight_shared_convolutional_box_predictor {
     conv_hyperparams {
       regularizer {
         l1_regularizer {
         }
       }
       batch_norm {
         train: true
       }
       initializer {
         truncated_normal_initializer {
         }
       }
     }
   }"""
     box_predictor_proto = box_predictor_pb2.BoxPredictor()
     text_format.Merge(box_predictor_text_proto, box_predictor_proto)
     box_predictor = box_predictor_builder.build(
         argscope_fn=hyperparams_builder.build,
         box_predictor_config=box_predictor_proto,
         is_training=True,
         num_classes=90)
     self.assertEqual(box_predictor._depth, 0)
     self.assertEqual(box_predictor._num_layers_before_predictor, 0)
     self.assertEqual(box_predictor.num_classes, 90)
     self.assertTrue(box_predictor._is_training)
     self.assertEqual(box_predictor._apply_batch_norm, True)
 def test_construct_weight_shared_predictor_with_default_mask_head(self):
     box_predictor_text_proto = """
   weight_shared_convolutional_box_predictor {
     mask_head {
     }
     conv_hyperparams {
       regularizer {
         l1_regularizer {
         }
       }
       initializer {
         truncated_normal_initializer {
         }
       }
     }
   }"""
     box_predictor_proto = box_predictor_pb2.BoxPredictor()
     text_format.Merge(box_predictor_text_proto, box_predictor_proto)
     box_predictor = box_predictor_builder.build(
         argscope_fn=hyperparams_builder.build,
         box_predictor_config=box_predictor_proto,
         is_training=True,
         num_classes=90)
     self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
                     box_predictor._other_heads)
     weight_shared_convolutional_mask_head = (box_predictor._other_heads[
         convolutional_box_predictor.MASK_PREDICTIONS])
     self.assertIsInstance(weight_shared_convolutional_mask_head,
                           mask_head.WeightSharedConvolutionalMaskHead)
     self.assertEqual(weight_shared_convolutional_mask_head._mask_height,
                      15)
     self.assertEqual(weight_shared_convolutional_mask_head._mask_width, 15)
     self.assertTrue(
         weight_shared_convolutional_mask_head._masks_are_class_agnostic)
 def test_construct_default_conv_box_predictor_with_custom_mask_head(self):
     box_predictor_text_proto = """
   convolutional_box_predictor {
     mask_head {
       mask_height: 7
       mask_width: 7
       masks_are_class_agnostic: false
     }
     conv_hyperparams {
       regularizer {
         l1_regularizer {
         }
       }
       initializer {
         truncated_normal_initializer {
         }
       }
     }
   }"""
     box_predictor_proto = box_predictor_pb2.BoxPredictor()
     text_format.Merge(box_predictor_text_proto, box_predictor_proto)
     box_predictor = box_predictor_builder.build(
         argscope_fn=hyperparams_builder.build,
         box_predictor_config=box_predictor_proto,
         is_training=True,
         num_classes=90)
     self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
                     box_predictor._other_heads)
     mask_prediction_head = (box_predictor._other_heads[
         convolutional_box_predictor.MASK_PREDICTIONS])
     self.assertEqual(mask_prediction_head._mask_height, 7)
     self.assertEqual(mask_prediction_head._mask_width, 7)
     self.assertFalse(mask_prediction_head._masks_are_class_agnostic)
Ejemplo n.º 9
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 def test_build_box_predictor_with_mask_branch(self):
   box_predictor_proto = box_predictor_pb2.BoxPredictor()
   box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
       hyperparams_pb2.Hyperparams.FC)
   box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams.op = (
       hyperparams_pb2.Hyperparams.CONV)
   box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
   box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
   box_predictor_proto.mask_rcnn_box_predictor.mask_height = 16
   box_predictor_proto.mask_rcnn_box_predictor.mask_width = 16
   mock_argscope_fn = mock.Mock(return_value='arg_scope')
   box_predictor = box_predictor_builder.build(
       argscope_fn=mock_argscope_fn,
       box_predictor_config=box_predictor_proto,
       is_training=True,
       num_classes=90)
   mock_argscope_fn.assert_has_calls(
       [mock.call(box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams, ),
        mock.call(box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams, )], any_order=True)
   box_head = box_predictor._box_prediction_head
   class_head = box_predictor._class_prediction_head
   third_stage_heads = box_predictor._third_stage_heads
   self.assertFalse(box_head._use_dropout)
   self.assertFalse(class_head._use_dropout)
   self.assertAlmostEqual(box_head._dropout_keep_prob, 0.5)
   self.assertAlmostEqual(class_head._dropout_keep_prob, 0.5)
   self.assertEqual(box_predictor.num_classes, 90)
   self.assertTrue(box_predictor._is_training)
   self.assertEqual(box_head._box_code_size, 4)
   self.assertTrue(
       mask_rcnn_box_predictor.MASK_PREDICTIONS in third_stage_heads)
   self.assertEqual(
       third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
       ._mask_prediction_conv_depth, 512)
Ejemplo n.º 10
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 def _get_second_stage_box_predictor(self, num_classes, is_training):
     box_predictor_proto = box_predictor_pb2.BoxPredictor()
     text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
                       box_predictor_proto)
     return box_predictor_builder.build(hyperparams_builder.build,
                                        box_predictor_proto,
                                        num_classes=num_classes,
                                        is_training=is_training)
Ejemplo n.º 11
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    def test_construct_non_default_conv_box_predictor(self):
        box_predictor_text_proto = """
      convolutional_box_predictor {
        min_depth: 2
        max_depth: 16
        num_layers_before_predictor: 2
        use_dropout: false
        dropout_keep_probability: 0.4
        kernel_size: 3
        box_code_size: 3
        apply_sigmoid_to_scores: true
        class_prediction_bias_init: 4.0
        use_depthwise: true
      }
    """
        conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
        hyperparams_proto = hyperparams_pb2.Hyperparams()
        text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)

        def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
            return (conv_hyperparams_arg, is_training)

        box_predictor_proto = box_predictor_pb2.BoxPredictor()
        text_format.Merge(box_predictor_text_proto, box_predictor_proto)
        box_predictor_proto.convolutional_box_predictor.conv_hyperparams.CopyFrom(
            hyperparams_proto)
        box_predictor = box_predictor_builder.build(
            argscope_fn=mock_conv_argscope_builder,
            box_predictor_config=box_predictor_proto,
            is_training=False,
            num_classes=10,
            add_background_class=False)
        class_head = box_predictor._class_prediction_head
        self.assertEqual(box_predictor._min_depth, 2)
        self.assertEqual(box_predictor._max_depth, 16)
        self.assertEqual(box_predictor._num_layers_before_predictor, 2)
        self.assertFalse(class_head._use_dropout)
        self.assertAlmostEqual(class_head._dropout_keep_prob, 0.4)
        self.assertTrue(class_head._apply_sigmoid_to_scores)
        self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
        self.assertEqual(class_head._num_class_slots, 10)
        self.assertEqual(box_predictor.num_classes, 10)
        self.assertFalse(box_predictor._is_training)
        self.assertTrue(class_head._use_depthwise)
 def test_build_default_mask_rcnn_box_predictor(self):
   box_predictor_proto = box_predictor_pb2.BoxPredictor()
   box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
       hyperparams_pb2.Hyperparams.FC)
   box_predictor = box_predictor_builder.build(
       argscope_fn=mock.Mock(return_value='arg_scope'),
       box_predictor_config=box_predictor_proto,
       is_training=True,
       num_classes=90)
   self.assertFalse(box_predictor._use_dropout)
   self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.5)
   self.assertEqual(box_predictor.num_classes, 90)
   self.assertTrue(box_predictor._is_training)
   self.assertEqual(box_predictor._box_code_size, 4)
   self.assertFalse(box_predictor._predict_instance_masks)
   self.assertFalse(box_predictor._predict_keypoints)
Ejemplo n.º 13
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    def test_non_default_mask_rcnn_box_predictor(self):
        fc_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
      op: FC
    """
        box_predictor_text_proto = """
      mask_rcnn_box_predictor {
        use_dropout: true
        dropout_keep_probability: 0.8
        box_code_size: 3
        share_box_across_classes: true
      }
    """
        hyperparams_proto = hyperparams_pb2.Hyperparams()
        text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)

        def mock_fc_argscope_builder(fc_hyperparams_arg, is_training):
            return (fc_hyperparams_arg, is_training)

        box_predictor_proto = box_predictor_pb2.BoxPredictor()
        text_format.Merge(box_predictor_text_proto, box_predictor_proto)
        box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
            hyperparams_proto)
        box_predictor = box_predictor_builder.build(
            argscope_fn=mock_fc_argscope_builder,
            box_predictor_config=box_predictor_proto,
            is_training=True,
            num_classes=90)
        box_head = box_predictor._box_prediction_head
        class_head = box_predictor._class_prediction_head
        self.assertTrue(box_head._use_dropout)
        self.assertTrue(class_head._use_dropout)
        self.assertAlmostEqual(box_head._dropout_keep_prob, 0.8)
        self.assertAlmostEqual(class_head._dropout_keep_prob, 0.8)
        self.assertEqual(box_predictor.num_classes, 90)
        self.assertTrue(box_predictor._is_training)
        self.assertEqual(box_head._box_code_size, 3)
        self.assertEqual(box_head._share_box_across_classes, True)
Ejemplo n.º 14
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    def test_box_predictor_calls_fc_argscope_fn(self):
        conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
          weight: 0.0003
        }
      }
      initializer {
        truncated_normal_initializer {
          mean: 0.0
          stddev: 0.3
        }
      }
      activation: RELU_6
    """
        hyperparams_proto = hyperparams_pb2.Hyperparams()
        text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)

        def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
            return (conv_hyperparams_arg, is_training)

        box_predictor_proto = box_predictor_pb2.BoxPredictor()
        box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
            hyperparams_proto)
        box_predictor = box_predictor_builder.build(
            argscope_fn=mock_conv_argscope_builder,
            box_predictor_config=box_predictor_proto,
            is_training=False,
            num_classes=10)
        (conv_hyperparams_actual,
         is_training) = box_predictor._conv_hyperparams_fn
        self.assertAlmostEqual(
            (hyperparams_proto.regularizer.l1_regularizer.weight),
            (conv_hyperparams_actual.regularizer.l1_regularizer.weight))
        self.assertAlmostEqual((
            hyperparams_proto.initializer.truncated_normal_initializer.stddev),
                               (conv_hyperparams_actual.initializer.
                                truncated_normal_initializer.stddev))
        self.assertAlmostEqual(
            (hyperparams_proto.initializer.truncated_normal_initializer.mean),
            (conv_hyperparams_actual.initializer.truncated_normal_initializer.
             mean))
        self.assertEqual(hyperparams_proto.activation,
                         conv_hyperparams_actual.activation)
        self.assertFalse(is_training)
Ejemplo n.º 15
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    def test_construct_non_default_depthwise_conv_box_predictor(self):
        box_predictor_text_proto = """
      weight_shared_convolutional_box_predictor {
        depth: 2
        num_layers_before_predictor: 2
        kernel_size: 7
        box_code_size: 3
        class_prediction_bias_init: 4.0
        use_depthwise: true
      }
    """
        conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
        hyperparams_proto = hyperparams_pb2.Hyperparams()
        text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)

        def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
            return (conv_hyperparams_arg, is_training)

        box_predictor_proto = box_predictor_pb2.BoxPredictor()
        text_format.Merge(box_predictor_text_proto, box_predictor_proto)
        (box_predictor_proto.weight_shared_convolutional_box_predictor.
         conv_hyperparams.CopyFrom(hyperparams_proto))
        box_predictor = box_predictor_builder.build(
            argscope_fn=mock_conv_argscope_builder,
            box_predictor_config=box_predictor_proto,
            is_training=False,
            num_classes=10,
            add_background_class=False)
        class_head = box_predictor._class_prediction_head
        self.assertEqual(box_predictor._depth, 2)
        self.assertEqual(box_predictor._num_layers_before_predictor, 2)
        self.assertEqual(box_predictor._apply_batch_norm, False)
        self.assertEqual(box_predictor._use_depthwise, True)
        self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
        self.assertEqual(box_predictor.num_classes, 10)
        self.assertFalse(box_predictor._is_training)