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._box_prediction_head._fc_hyperparams_fn,
'arg_scope')
self.assertEqual(
box_predictor._class_prediction_head._fc_hyperparams_fn,
'arg_scope')
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_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_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)
self.assertFalse(box_predictor._use_depthwise)
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)
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(box_predictor._use_dropout)
self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.4)
self.assertTrue(box_predictor._apply_sigmoid_to_scores)
self.assertAlmostEqual(box_predictor._class_prediction_bias_init, 4.0)
self.assertEqual(box_predictor.num_classes, 10)
self.assertFalse(box_predictor._is_training)
self.assertTrue(box_predictor._use_depthwise)
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)
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)
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)
box_head = box_predictor._box_prediction_head
class_head = box_predictor._class_prediction_head
self.assertFalse(box_head._use_dropout)
self.assertFalse(class_head._use_dropout)
self.assertAlmostEqual(box_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.assertEqual(len(box_predictor._third_stage_heads.keys()), 0)
def test_construct_non_default_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
}
"""
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)
self.assertEqual(box_predictor._depth, 2)
self.assertEqual(box_predictor._num_layers_before_predictor, 2)
self.assertAlmostEqual(box_predictor._class_prediction_bias_init, 4.0)
self.assertEqual(box_predictor.num_classes, 10)
self.assertFalse(box_predictor._is_training)
self.assertEqual(box_predictor._apply_batch_norm, False)
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,
True),
mock.call(
box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams,
True)
],
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)