def build(matcher_config):
"""Builds a matcher object based on the matcher config.
Args:
matcher_config: A matcher.proto object containing the config for the desired
Matcher.
Returns:
Matcher based on the config.
Raises:
ValueError: On empty matcher proto.
"""
if not isinstance(matcher_config, matcher_pb2.Matcher):
raise ValueError('matcher_config not of type matcher_pb2.Matcher.')
if matcher_config.WhichOneof('matcher_oneof') == 'argmax_matcher':
matcher = matcher_config.argmax_matcher
matched_threshold = unmatched_threshold = None
if not matcher.ignore_thresholds:
matched_threshold = matcher.matched_threshold
unmatched_threshold = matcher.unmatched_threshold
return argmax_matcher.ArgMaxMatcher(
matched_threshold=matched_threshold,
unmatched_threshold=unmatched_threshold,
negatives_lower_than_unmatched=matcher.
negatives_lower_than_unmatched,
force_match_for_each_row=matcher.force_match_for_each_row,
use_matmul_gather=matcher.use_matmul_gather)
if matcher_config.WhichOneof('matcher_oneof') == 'bipartite_matcher':
if tf_version.is_tf2():
raise ValueError('bipartite_matcher is not supported in TF 2.X')
matcher = matcher_config.bipartite_matcher
return bipartite_matcher.GreedyBipartiteMatcher(
matcher.use_matmul_gather)
raise ValueError('Empty matcher.')
def _build_feature_map_generator(self,
feature_map_layout,
pool_residual=False):
if tf_version.is_tf2():
return feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
freeze_batchnorm=False,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
name='FeatureMaps')
else:
def feature_map_generator(image_features):
return feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
image_features=image_features,
pool_residual=pool_residual)
return feature_map_generator
def _build_feature_map_generator(self,
image_features,
depth,
use_bounded_activations=False,
use_native_resize_op=False,
use_explicit_padding=False,
use_depthwise=False):
if tf_version.is_tf2():
return feature_map_generators.KerasFpnTopDownFeatureMaps(
num_levels=len(image_features),
depth=depth,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
use_depthwise=use_depthwise,
use_explicit_padding=use_explicit_padding,
use_bounded_activations=use_bounded_activations,
use_native_resize_op=use_native_resize_op,
scope=None,
name='FeatureMaps',
)
else:
def feature_map_generator(image_features):
return feature_map_generators.fpn_top_down_feature_maps(
image_features=image_features,
depth=depth,
use_depthwise=use_depthwise,
use_explicit_padding=use_explicit_padding,
use_bounded_activations=use_bounded_activations,
use_native_resize_op=use_native_resize_op)
return feature_map_generator
def test_get_expected_variable_names_with_inception_v2(self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256],
dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576],
dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024],
dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V2_LAYOUT, )
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'Mixed_5c_1_Conv2d_3_1x1_256/weights',
'Mixed_5c_1_Conv2d_3_1x1_256/biases',
'Mixed_5c_2_Conv2d_3_3x3_s2_512/weights',
'Mixed_5c_2_Conv2d_3_3x3_s2_512/biases',
'Mixed_5c_1_Conv2d_4_1x1_128/weights',
'Mixed_5c_1_Conv2d_4_1x1_128/biases',
'Mixed_5c_2_Conv2d_4_3x3_s2_256/weights',
'Mixed_5c_2_Conv2d_4_3x3_s2_256/biases',
'Mixed_5c_1_Conv2d_5_1x1_128/weights',
'Mixed_5c_1_Conv2d_5_1x1_128/biases',
'Mixed_5c_2_Conv2d_5_3x3_s2_256/weights',
'Mixed_5c_2_Conv2d_5_3x3_s2_256/biases',
])
expected_keras_variables = set([
'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/kernel',
'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/bias',
'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/kernel',
'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/bias',
'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/kernel',
'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/bias',
'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/kernel',
'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/bias',
'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/kernel',
'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/bias',
'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/kernel',
'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',
])
if tf_version.is_tf2():
actual_variable_set = set([
var.name.split(':')[0]
for var in feature_map_generator.variables
])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
def test_build_bipartite_matcher(self):
if tf_version.is_tf2():
self.skipTest('BipartiteMatcher unsupported in TF 2.X. Skipping.')
matcher_text_proto = """
bipartite_matcher {
}
"""
matcher_proto = matcher_pb2.Matcher()
text_format.Merge(matcher_text_proto, matcher_proto)
matcher_object = matcher_builder.build(matcher_proto)
self.assertIsInstance(matcher_object,
bipartite_matcher.GreedyBipartiteMatcher)
def __exit__(self, ttype, value, traceback):
if tf_version.is_tf2():
return False
else:
return self.graph.__exit__(ttype, value, traceback)
def __enter__(self):
if tf_version.is_tf2():
return None
else:
return self.graph.__enter__()
def __init__(self):
if tf_version.is_tf2():
self.graph = None
else:
self.graph = tf.Graph().as_default()
def materialize_tensors(self, list_of_tensors):
if tf_version.is_tf2():
return [tensor.numpy() for tensor in list_of_tensors]
else:
with self.cached_session() as sess:
return sess.run(list_of_tensors)
def _build_ssd_feature_extractor(feature_extractor_config,
is_training,
freeze_batchnorm,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
freeze_batchnorm: Whether to freeze batch norm parameters during
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
pad_to_multiple = feature_extractor_config.pad_to_multiple
use_explicit_padding = feature_extractor_config.use_explicit_padding
use_depthwise = feature_extractor_config.use_depthwise
is_keras = tf_version.is_tf2()
if is_keras:
conv_hyperparams = hyperparams_builder.KerasLayerHyperparams(
feature_extractor_config.conv_hyperparams)
else:
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
override_base_feature_extractor_hyperparams = (
feature_extractor_config.override_base_feature_extractor_hyperparams)
if not is_keras and feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError(
'Unknown ssd feature_extractor: {}'.format(feature_type))
if is_keras:
feature_extractor_class = SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP[
feature_type]
else:
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
kwargs = {
'is_training':
is_training,
'depth_multiplier':
depth_multiplier,
'min_depth':
min_depth,
'pad_to_multiple':
pad_to_multiple,
'use_explicit_padding':
use_explicit_padding,
'use_depthwise':
use_depthwise,
'override_base_feature_extractor_hyperparams':
override_base_feature_extractor_hyperparams
}
if feature_extractor_config.HasField(
'replace_preprocessor_with_placeholder'):
kwargs.update({
'replace_preprocessor_with_placeholder':
feature_extractor_config.replace_preprocessor_with_placeholder
})
if feature_extractor_config.HasField('num_layers'):
kwargs.update({'num_layers': feature_extractor_config.num_layers})
if is_keras:
kwargs.update({
'conv_hyperparams': conv_hyperparams,
'inplace_batchnorm_update': False,
'freeze_batchnorm': freeze_batchnorm
})
else:
kwargs.update({
'conv_hyperparams_fn': conv_hyperparams,
'reuse_weights': reuse_weights,
})
if feature_extractor_config.HasField('fpn'):
kwargs.update({
'fpn_min_level':
feature_extractor_config.fpn.min_level,
'fpn_max_level':
feature_extractor_config.fpn.max_level,
'additional_layer_depth':
feature_extractor_config.fpn.additional_layer_depth,
})
if feature_extractor_config.HasField('bifpn'):
kwargs.update({
'bifpn_min_level':
feature_extractor_config.bifpn.min_level,
'bifpn_max_level':
feature_extractor_config.bifpn.max_level,
'bifpn_num_iterations':
feature_extractor_config.bifpn.num_iterations,
'bifpn_num_filters':
feature_extractor_config.bifpn.num_filters,
'bifpn_combine_method':
feature_extractor_config.bifpn.combine_method,
})
return feature_extractor_class(**kwargs)
import numpy as np
import tensorflow.compat.v1 as tf
import tf_slim as slim
from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.core import freezable_batch_norm
from object_detection.protos import hyperparams_pb2
from object_detection.utils import tf_version
def _get_scope_key(op):
return getattr(op, '_key_op', str(op))
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only tests.')
class HyperparamsBuilderTest(tf.test.TestCase):
def test_default_arg_scope_has_conv2d_op(self):
conv_hyperparams_text_proto = """
regularizer {
l1_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
def _create_model(
self,
model_fn=ssd_meta_arch.SSDMetaArch,
apply_hard_mining=True,
normalize_loc_loss_by_codesize=False,
add_background_class=True,
random_example_sampling=False,
expected_loss_weights=model_pb2.DetectionModel().ssd.loss.NONE,
min_num_negative_samples=1,
desired_negative_sampling_ratio=3,
predict_mask=False,
use_static_shapes=False,
nms_max_size_per_class=5,
calibration_mapping_value=None,
return_raw_detections_during_predict=False):
is_training = False
num_classes = 1
mock_anchor_generator = MockAnchorGenerator2x2()
use_keras = tf_version.is_tf2()
if use_keras:
mock_box_predictor = test_utils.MockKerasBoxPredictor(
is_training,
num_classes,
add_background_class=add_background_class)
else:
mock_box_predictor = test_utils.MockBoxPredictor(
is_training,
num_classes,
add_background_class=add_background_class)
mock_box_coder = test_utils.MockBoxCoder()
if use_keras:
fake_feature_extractor = FakeSSDKerasFeatureExtractor()
else:
fake_feature_extractor = FakeSSDFeatureExtractor()
mock_matcher = test_utils.MockMatcher()
region_similarity_calculator = sim_calc.IouSimilarity()
encode_background_as_zeros = False
def image_resizer_fn(image):
return [tf.identity(image), tf.shape(image)]
classification_loss = losses.WeightedSigmoidClassificationLoss()
localization_loss = losses.WeightedSmoothL1LocalizationLoss()
non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=-20.0,
iou_thresh=1.0,
max_size_per_class=nms_max_size_per_class,
max_total_size=nms_max_size_per_class,
use_static_shapes=use_static_shapes)
score_conversion_fn = tf.identity
calibration_config = calibration_pb2.CalibrationConfig()
if calibration_mapping_value:
calibration_text_proto = """
function_approximation {
x_y_pairs {
x_y_pair {
x: 0.0
y: %f
}
x_y_pair {
x: 1.0
y: %f
}}}""" % (calibration_mapping_value, calibration_mapping_value)
text_format.Merge(calibration_text_proto, calibration_config)
score_conversion_fn = (
post_processing_builder._build_calibrated_score_converter( # pylint: disable=protected-access
tf.identity, calibration_config))
classification_loss_weight = 1.0
localization_loss_weight = 1.0
negative_class_weight = 1.0
normalize_loss_by_num_matches = False
hard_example_miner = None
if apply_hard_mining:
# This hard example miner is expected to be a no-op.
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=None, iou_threshold=1.0)
random_example_sampler = None
if random_example_sampling:
random_example_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=0.5)
target_assigner_instance = target_assigner.TargetAssigner(
region_similarity_calculator,
mock_matcher,
mock_box_coder,
negative_class_weight=negative_class_weight)
model_config = model_pb2.DetectionModel()
if expected_loss_weights == model_config.ssd.loss.NONE:
expected_loss_weights_fn = None
else:
raise ValueError('Not a valid value for expected_loss_weights.')
code_size = 4
kwargs = {}
if predict_mask:
kwargs.update({
'mask_prediction_fn':
test_utils.MockMaskHead(num_classes=1).predict,
})
model = model_fn(
is_training=is_training,
anchor_generator=mock_anchor_generator,
box_predictor=mock_box_predictor,
box_coder=mock_box_coder,
feature_extractor=fake_feature_extractor,
encode_background_as_zeros=encode_background_as_zeros,
image_resizer_fn=image_resizer_fn,
non_max_suppression_fn=non_max_suppression_fn,
score_conversion_fn=score_conversion_fn,
classification_loss=classification_loss,
localization_loss=localization_loss,
classification_loss_weight=classification_loss_weight,
localization_loss_weight=localization_loss_weight,
normalize_loss_by_num_matches=normalize_loss_by_num_matches,
hard_example_miner=hard_example_miner,
target_assigner_instance=target_assigner_instance,
add_summaries=False,
normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize,
freeze_batchnorm=False,
inplace_batchnorm_update=False,
add_background_class=add_background_class,
random_example_sampler=random_example_sampler,
expected_loss_weights_fn=expected_loss_weights_fn,
return_raw_detections_during_predict=(
return_raw_detections_during_predict),
**kwargs)
return model, num_classes, mock_anchor_generator.num_anchors(
), code_size
class ContextRCNNMetaArchTest(test_case.TestCase, parameterized.TestCase):
def _get_model(self, box_predictor, **common_kwargs):
return context_rcnn_meta_arch.ContextRCNNMetaArch(
initial_crop_size=3,
maxpool_kernel_size=1,
maxpool_stride=1,
second_stage_mask_rcnn_box_predictor=box_predictor,
attention_bottleneck_dimension=10,
attention_temperature=0.2,
**common_kwargs)
def _build_arg_scope_with_hyperparams(self, hyperparams_text_proto,
is_training):
hyperparams = hyperparams_pb2.Hyperparams()
text_format.Merge(hyperparams_text_proto, hyperparams)
return hyperparams_builder.build(hyperparams, is_training=is_training)
def _build_keras_layer_hyperparams(self, hyperparams_text_proto):
hyperparams = hyperparams_pb2.Hyperparams()
text_format.Merge(hyperparams_text_proto, hyperparams)
return hyperparams_builder.KerasLayerHyperparams(hyperparams)
def _get_second_stage_box_predictor_text_proto(
self, share_box_across_classes=False):
share_box_field = 'true' if share_box_across_classes else 'false'
box_predictor_text_proto = """
mask_rcnn_box_predictor {{
fc_hyperparams {{
op: FC
activation: NONE
regularizer {{
l2_regularizer {{
weight: 0.0005
}}
}}
initializer {{
variance_scaling_initializer {{
factor: 1.0
uniform: true
mode: FAN_AVG
}}
}}
}}
share_box_across_classes: {share_box_across_classes}
}}
""".format(share_box_across_classes=share_box_field)
return box_predictor_text_proto
def _get_box_classifier_features_shape(self, image_size, batch_size,
max_num_proposals,
initial_crop_size, maxpool_stride,
num_features):
return (batch_size * max_num_proposals,
initial_crop_size / maxpool_stride,
initial_crop_size / maxpool_stride, num_features)
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 _build_model(self,
is_training,
number_of_stages,
second_stage_batch_size,
first_stage_max_proposals=8,
num_classes=2,
hard_mining=False,
softmax_second_stage_classification_loss=True,
predict_masks=False,
pad_to_max_dimension=None,
masks_are_class_agnostic=False,
use_matmul_crop_and_resize=False,
clip_anchors_to_image=False,
use_matmul_gather_in_matcher=False,
use_static_shapes=False,
calibration_mapping_value=None,
share_box_across_classes=False,
return_raw_detections_during_predict=False):
use_keras = tf_version.is_tf2()
def image_resizer_fn(image, masks=None):
"""Fake image resizer function."""
resized_inputs = []
resized_image = tf.identity(image)
if pad_to_max_dimension is not None:
resized_image = tf.image.pad_to_bounding_box(
image, 0, 0, pad_to_max_dimension, pad_to_max_dimension)
resized_inputs.append(resized_image)
if masks is not None:
resized_masks = tf.identity(masks)
if pad_to_max_dimension is not None:
resized_masks = tf.image.pad_to_bounding_box(
tf.transpose(masks, [1, 2, 0]), 0, 0,
pad_to_max_dimension, pad_to_max_dimension)
resized_masks = tf.transpose(resized_masks, [2, 0, 1])
resized_inputs.append(resized_masks)
resized_inputs.append(tf.shape(image))
return resized_inputs
# anchors in this test are designed so that a subset of anchors are inside
# the image and a subset of anchors are outside.
first_stage_anchor_scales = (0.001, 0.005, 0.1)
first_stage_anchor_aspect_ratios = (0.5, 1.0, 2.0)
first_stage_anchor_strides = (1, 1)
first_stage_anchor_generator = grid_anchor_generator.GridAnchorGenerator(
first_stage_anchor_scales,
first_stage_anchor_aspect_ratios,
anchor_stride=first_stage_anchor_strides)
first_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'proposal',
use_matmul_gather=use_matmul_gather_in_matcher)
if use_keras:
fake_feature_extractor = FakeFasterRCNNKerasFeatureExtractor()
else:
fake_feature_extractor = FakeFasterRCNNFeatureExtractor()
first_stage_box_predictor_hyperparams_text_proto = """
op: CONV
activation: RELU
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
}
}
"""
if use_keras:
first_stage_box_predictor_arg_scope_fn = (
self._build_keras_layer_hyperparams(
first_stage_box_predictor_hyperparams_text_proto))
else:
first_stage_box_predictor_arg_scope_fn = (
self._build_arg_scope_with_hyperparams(
first_stage_box_predictor_hyperparams_text_proto,
is_training))
first_stage_box_predictor_kernel_size = 3
first_stage_atrous_rate = 1
first_stage_box_predictor_depth = 512
first_stage_minibatch_size = 3
first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=0.5, is_static=use_static_shapes)
first_stage_nms_score_threshold = -1.0
first_stage_nms_iou_threshold = 1.0
first_stage_max_proposals = first_stage_max_proposals
first_stage_non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=first_stage_nms_score_threshold,
iou_thresh=first_stage_nms_iou_threshold,
max_size_per_class=first_stage_max_proposals,
max_total_size=first_stage_max_proposals,
use_static_shapes=use_static_shapes)
first_stage_localization_loss_weight = 1.0
first_stage_objectness_loss_weight = 1.0
post_processing_config = post_processing_pb2.PostProcessing()
post_processing_text_proto = """
score_converter: IDENTITY
batch_non_max_suppression {
score_threshold: -20.0
iou_threshold: 1.0
max_detections_per_class: 5
max_total_detections: 5
use_static_shapes: """ + '{}'.format(use_static_shapes) + """
}
"""
if calibration_mapping_value:
calibration_text_proto = """
calibration_config {
function_approximation {
x_y_pairs {
x_y_pair {
x: 0.0
y: %f
}
x_y_pair {
x: 1.0
y: %f
}}}}""" % (calibration_mapping_value, calibration_mapping_value)
post_processing_text_proto = (post_processing_text_proto + ' ' +
calibration_text_proto)
text_format.Merge(post_processing_text_proto, post_processing_config)
second_stage_non_max_suppression_fn, second_stage_score_conversion_fn = (
post_processing_builder.build(post_processing_config))
second_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'detection',
use_matmul_gather=use_matmul_gather_in_matcher)
second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=1.0, is_static=use_static_shapes)
second_stage_localization_loss_weight = 1.0
second_stage_classification_loss_weight = 1.0
if softmax_second_stage_classification_loss:
second_stage_classification_loss = (
losses.WeightedSoftmaxClassificationLoss())
else:
second_stage_classification_loss = (
losses.WeightedSigmoidClassificationLoss())
hard_example_miner = None
if hard_mining:
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=1,
iou_threshold=0.99,
loss_type='both',
cls_loss_weight=second_stage_classification_loss_weight,
loc_loss_weight=second_stage_localization_loss_weight,
max_negatives_per_positive=None)
crop_and_resize_fn = (spatial_ops.multilevel_matmul_crop_and_resize
if use_matmul_crop_and_resize else
spatial_ops.multilevel_native_crop_and_resize)
common_kwargs = {
'is_training':
is_training,
'num_classes':
num_classes,
'image_resizer_fn':
image_resizer_fn,
'feature_extractor':
fake_feature_extractor,
'number_of_stages':
number_of_stages,
'first_stage_anchor_generator':
first_stage_anchor_generator,
'first_stage_target_assigner':
first_stage_target_assigner,
'first_stage_atrous_rate':
first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope_fn':
first_stage_box_predictor_arg_scope_fn,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth':
first_stage_box_predictor_depth,
'first_stage_minibatch_size':
first_stage_minibatch_size,
'first_stage_sampler':
first_stage_sampler,
'first_stage_non_max_suppression_fn':
first_stage_non_max_suppression_fn,
'first_stage_max_proposals':
first_stage_max_proposals,
'first_stage_localization_loss_weight':
first_stage_localization_loss_weight,
'first_stage_objectness_loss_weight':
first_stage_objectness_loss_weight,
'second_stage_target_assigner':
second_stage_target_assigner,
'second_stage_batch_size':
second_stage_batch_size,
'second_stage_sampler':
second_stage_sampler,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn':
second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'second_stage_classification_loss':
second_stage_classification_loss,
'hard_example_miner':
hard_example_miner,
'crop_and_resize_fn':
crop_and_resize_fn,
'clip_anchors_to_image':
clip_anchors_to_image,
'use_static_shapes':
use_static_shapes,
'resize_masks':
True,
'return_raw_detections_during_predict':
return_raw_detections_during_predict
}
return self._get_model(
self._get_second_stage_box_predictor(
num_classes=num_classes,
is_training=is_training,
use_keras=use_keras,
predict_masks=predict_masks,
masks_are_class_agnostic=masks_are_class_agnostic,
share_box_across_classes=share_box_across_classes),
**common_kwargs)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
@mock.patch.object(context_rcnn_meta_arch, 'context_rcnn_lib')
def test_prediction_mock_tf1(self, mock_context_rcnn_lib_v1):
"""Mocks the context_rcnn_lib_v1 module to test the prediction.
Using mock object so that we can ensure compute_box_context_attention is
called in side the prediction function.
Args:
mock_context_rcnn_lib_v1: mock module for the context_rcnn_lib_v1.
"""
model = self._build_model(is_training=False,
number_of_stages=2,
second_stage_batch_size=6,
num_classes=42)
mock_tensor = tf.ones([2, 8, 3, 3, 3], tf.float32)
mock_context_rcnn_lib_v1.compute_box_context_attention.return_value = mock_tensor
inputs_shape = (2, 20, 20, 3)
inputs = tf.cast(tf.random_uniform(inputs_shape,
minval=0,
maxval=255,
dtype=tf.int32),
dtype=tf.float32)
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
context_features = tf.random_uniform((2, 20, 10),
minval=0,
maxval=255,
dtype=tf.float32)
valid_context_size = tf.random_uniform((2, ),
minval=0,
maxval=10,
dtype=tf.int32)
features = {
fields.InputDataFields.context_features: context_features,
fields.InputDataFields.valid_context_size: valid_context_size
}
side_inputs = model.get_side_inputs(features)
_ = model.predict(preprocessed_inputs, true_image_shapes,
**side_inputs)
mock_context_rcnn_lib_v1.compute_box_context_attention.assert_called_once(
)
@parameterized.named_parameters(
{
'testcase_name': 'static_shapes',
'static_shapes': True
},
{
'testcase_name': 'nostatic_shapes',
'static_shapes': False
},
)
def test_prediction_end_to_end(self, static_shapes):
"""Runs prediction end to end and test the shape of the results."""
with test_utils.GraphContextOrNone() as g:
model = self._build_model(
is_training=False,
number_of_stages=2,
second_stage_batch_size=6,
use_matmul_crop_and_resize=static_shapes,
clip_anchors_to_image=static_shapes,
use_matmul_gather_in_matcher=static_shapes,
use_static_shapes=static_shapes,
num_classes=42)
def graph_fn():
inputs_shape = (2, 20, 20, 3)
inputs = tf.cast(tf.random_uniform(inputs_shape,
minval=0,
maxval=255,
dtype=tf.int32),
dtype=tf.float32)
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
context_features = tf.random_uniform((2, 20, 10),
minval=0,
maxval=255,
dtype=tf.float32)
valid_context_size = tf.random_uniform((2, ),
minval=0,
maxval=10,
dtype=tf.int32)
features = {
fields.InputDataFields.context_features: context_features,
fields.InputDataFields.valid_context_size: valid_context_size
}
side_inputs = model.get_side_inputs(features)
prediction_dict = model.predict(preprocessed_inputs,
true_image_shapes, **side_inputs)
return (prediction_dict['rpn_box_predictor_features'],
prediction_dict['rpn_box_encodings'],
prediction_dict['refined_box_encodings'],
prediction_dict['proposal_boxes_normalized'],
prediction_dict['proposal_boxes'])
execute_fn = self.execute if static_shapes else self.execute_cpu
(rpn_box_predictor_features, rpn_box_encodings, refined_box_encodings,
proposal_boxes_normalized, proposal_boxes) = execute_fn(graph_fn, [],
graph=g)
self.assertAllEqual(rpn_box_predictor_features.shape, [2, 20, 20, 512])
self.assertAllEqual(rpn_box_encodings.shape, [2, 3600, 4])
self.assertAllEqual(refined_box_encodings.shape, [16, 42, 4])
self.assertAllEqual(proposal_boxes_normalized.shape, [2, 8, 4])
self.assertAllEqual(proposal_boxes.shape, [2, 8, 4])
def test_get_expected_variable_names_with_depthwise(
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'projection_1/weights',
'projection_1/biases',
'projection_2/weights',
'projection_2/biases',
'projection_3/weights',
'projection_3/biases',
'projection_4/weights',
'projection_4/biases',
'smoothing_1/depthwise_weights',
'smoothing_1/pointwise_weights',
'smoothing_1/biases',
'smoothing_2/depthwise_weights',
'smoothing_2/pointwise_weights',
'smoothing_2/biases',
'smoothing_3/depthwise_weights',
'smoothing_3/pointwise_weights',
'smoothing_3/biases',
])
expected_keras_variables = set([
'FeatureMaps/top_down/projection_1/kernel',
'FeatureMaps/top_down/projection_1/bias',
'FeatureMaps/top_down/projection_2/kernel',
'FeatureMaps/top_down/projection_2/bias',
'FeatureMaps/top_down/projection_3/kernel',
'FeatureMaps/top_down/projection_3/bias',
'FeatureMaps/top_down/projection_4/kernel',
'FeatureMaps/top_down/projection_4/bias',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/bias',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/bias',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/bias'
])
if tf_version.is_tf2():
actual_variable_set = set([
var.name.split(':')[0]
for var in feature_map_generator.variables
])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
class FPNFeatureMapGeneratorTest(test_case.TestCase, parameterized.TestCase):
def _build_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def _build_feature_map_generator(self,
image_features,
depth,
use_bounded_activations=False,
use_native_resize_op=False,
use_explicit_padding=False,
use_depthwise=False):
if tf_version.is_tf2():
return feature_map_generators.KerasFpnTopDownFeatureMaps(
num_levels=len(image_features),
depth=depth,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
use_depthwise=use_depthwise,
use_explicit_padding=use_explicit_padding,
use_bounded_activations=use_bounded_activations,
use_native_resize_op=use_native_resize_op,
scope=None,
name='FeatureMaps',
)
else:
def feature_map_generator(image_features):
return feature_map_generators.fpn_top_down_feature_maps(
image_features=image_features,
depth=depth,
use_depthwise=use_depthwise,
use_explicit_padding=use_explicit_padding,
use_bounded_activations=use_bounded_activations,
use_native_resize_op=use_native_resize_op)
return feature_map_generator
def test_get_expected_feature_map_shapes(self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
'top_down_block3': (4, 4, 4, 128),
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes,
out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_explicit_padding(
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_explicit_padding=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
'top_down_block3': (4, 4, 4, 128),
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes,
out_feature_map_shapes)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
def test_use_bounded_activations_add_operations(self,
use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_bounded_activations=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_added_operations = dict.fromkeys([
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
])
op_names = {op.name: None for op in g.get_operations()}
self.assertDictContainsSubset(expected_added_operations, op_names)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
def test_use_bounded_activations_clip_value(self, use_native_resize_op):
tf_graph = tf.Graph()
with tf_graph.as_default():
image_features = [
('block2', 255 * tf.ones([4, 8, 8, 256], dtype=tf.float32)),
('block3', 255 * tf.ones([4, 4, 4, 256], dtype=tf.float32)),
('block4', 255 * tf.ones([4, 2, 2, 256], dtype=tf.float32)),
('block5', 255 * tf.ones([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_bounded_activations=True,
use_native_resize_op=use_native_resize_op)
feature_map_generator(image_features)
expected_clip_by_value_ops = [
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
]
# Gathers activation tensors before and after clip_by_value operations.
activations = {}
for clip_by_value_op in expected_clip_by_value_ops:
clip_input_tensor = tf_graph.get_operation_by_name(
'{}/Minimum'.format(clip_by_value_op)).inputs[0]
clip_output_tensor = tf_graph.get_tensor_by_name(
'{}:0'.format(clip_by_value_op))
activations.update({
'before_{}'.format(clip_by_value_op):
clip_input_tensor,
'after_{}'.format(clip_by_value_op):
clip_output_tensor,
})
expected_lower_bound = -feature_map_generators.ACTIVATION_BOUND
expected_upper_bound = feature_map_generators.ACTIVATION_BOUND
init_op = tf.global_variables_initializer()
with self.test_session() as session:
session.run(init_op)
activations_output = session.run(activations)
for clip_by_value_op in expected_clip_by_value_ops:
# Before clipping, activations are beyound the expected bound because
# of large input image_features values.
activations_before_clipping = (activations_output[
'before_{}'.format(clip_by_value_op)])
before_clipping_lower_bound = np.amin(
activations_before_clipping)
before_clipping_upper_bound = np.amax(
activations_before_clipping)
self.assertLessEqual(before_clipping_lower_bound,
expected_lower_bound)
self.assertGreaterEqual(before_clipping_upper_bound,
expected_upper_bound)
# After clipping, activations are bounded as expectation.
activations_after_clipping = (activations_output[
'after_{}'.format(clip_by_value_op)])
after_clipping_lower_bound = np.amin(
activations_after_clipping)
after_clipping_upper_bound = np.amax(
activations_after_clipping)
self.assertGreaterEqual(after_clipping_lower_bound,
expected_lower_bound)
self.assertLessEqual(after_clipping_upper_bound,
expected_upper_bound)
def test_get_expected_feature_map_shapes_with_depthwise(
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
'top_down_block3': (4, 4, 4, 128),
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes,
out_feature_map_shapes)
def test_get_expected_variable_names(self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'projection_1/weights',
'projection_1/biases',
'projection_2/weights',
'projection_2/biases',
'projection_3/weights',
'projection_3/biases',
'projection_4/weights',
'projection_4/biases',
'smoothing_1/weights',
'smoothing_1/biases',
'smoothing_2/weights',
'smoothing_2/biases',
'smoothing_3/weights',
'smoothing_3/biases',
])
expected_keras_variables = set([
'FeatureMaps/top_down/projection_1/kernel',
'FeatureMaps/top_down/projection_1/bias',
'FeatureMaps/top_down/projection_2/kernel',
'FeatureMaps/top_down/projection_2/bias',
'FeatureMaps/top_down/projection_3/kernel',
'FeatureMaps/top_down/projection_3/bias',
'FeatureMaps/top_down/projection_4/kernel',
'FeatureMaps/top_down/projection_4/bias',
'FeatureMaps/top_down/smoothing_1_conv/kernel',
'FeatureMaps/top_down/smoothing_1_conv/bias',
'FeatureMaps/top_down/smoothing_2_conv/kernel',
'FeatureMaps/top_down/smoothing_2_conv/bias',
'FeatureMaps/top_down/smoothing_3_conv/kernel',
'FeatureMaps/top_down/smoothing_3_conv/bias'
])
if tf_version.is_tf2():
actual_variable_set = set([
var.name.split(':')[0]
for var in feature_map_generator.variables
])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
def test_get_expected_variable_names_with_depthwise(
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256],
dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256],
dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256],
dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'projection_1/weights',
'projection_1/biases',
'projection_2/weights',
'projection_2/biases',
'projection_3/weights',
'projection_3/biases',
'projection_4/weights',
'projection_4/biases',
'smoothing_1/depthwise_weights',
'smoothing_1/pointwise_weights',
'smoothing_1/biases',
'smoothing_2/depthwise_weights',
'smoothing_2/pointwise_weights',
'smoothing_2/biases',
'smoothing_3/depthwise_weights',
'smoothing_3/pointwise_weights',
'smoothing_3/biases',
])
expected_keras_variables = set([
'FeatureMaps/top_down/projection_1/kernel',
'FeatureMaps/top_down/projection_1/bias',
'FeatureMaps/top_down/projection_2/kernel',
'FeatureMaps/top_down/projection_2/bias',
'FeatureMaps/top_down/projection_3/kernel',
'FeatureMaps/top_down/projection_3/bias',
'FeatureMaps/top_down/projection_4/kernel',
'FeatureMaps/top_down/projection_4/bias',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_1_depthwise_conv/bias',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_2_depthwise_conv/bias',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/depthwise_kernel',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/bias'
])
if tf_version.is_tf2():
actual_variable_set = set([
var.name.split(':')[0]
for var in feature_map_generator.variables
])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
for i in range(self.od_eval.num_class):
self.assertTrue(
np.allclose(copy_precisions_per_class[i],
precisions_per_class[i]))
self.assertTrue(
np.allclose(copy_recalls_per_class[i], recalls_per_class[i]))
self.assertTrue(
np.allclose(copy_average_precision_per_class,
average_precision_per_class))
self.assertTrue(np.allclose(copy_corloc_per_class, corloc_per_class))
self.assertAlmostEqual(copy_mean_ap, mean_ap)
self.assertAlmostEqual(copy_mean_corloc, mean_corloc)
@unittest.skipIf(tf_version.is_tf2(),
'Eval Metrics ops are supported in TF1.X '
'only.')
class ObjectDetectionEvaluatorTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
self.categories = [{
'id': 1,
'name': 'person'
}, {
'id': 2,
'name': 'dog'
}, {
'id': 3,
'name': 'cat'
}]
self.od_eval = object_detection_evaluation.ObjectDetectionEvaluator(
def is_tf2(self):
"""Returns whether TF2 is enabled."""
return tf_version.is_tf2()
def _build_model(self,
is_training,
number_of_stages,
second_stage_batch_size,
first_stage_max_proposals=8,
num_classes=2,
hard_mining=False,
softmax_second_stage_classification_loss=True,
predict_masks=False,
pad_to_max_dimension=None,
masks_are_class_agnostic=False,
use_matmul_crop_and_resize=False,
clip_anchors_to_image=False,
use_matmul_gather_in_matcher=False,
use_static_shapes=False,
calibration_mapping_value=None,
share_box_across_classes=False,
return_raw_detections_during_predict=False):
use_keras = tf_version.is_tf2()
def image_resizer_fn(image, masks=None):
"""Fake image resizer function."""
resized_inputs = []
resized_image = tf.identity(image)
if pad_to_max_dimension is not None:
resized_image = tf.image.pad_to_bounding_box(
image, 0, 0, pad_to_max_dimension, pad_to_max_dimension)
resized_inputs.append(resized_image)
if masks is not None:
resized_masks = tf.identity(masks)
if pad_to_max_dimension is not None:
resized_masks = tf.image.pad_to_bounding_box(
tf.transpose(masks, [1, 2, 0]), 0, 0,
pad_to_max_dimension, pad_to_max_dimension)
resized_masks = tf.transpose(resized_masks, [2, 0, 1])
resized_inputs.append(resized_masks)
resized_inputs.append(tf.shape(image))
return resized_inputs
# anchors in this test are designed so that a subset of anchors are inside
# the image and a subset of anchors are outside.
first_stage_anchor_scales = (0.001, 0.005, 0.1)
first_stage_anchor_aspect_ratios = (0.5, 1.0, 2.0)
first_stage_anchor_strides = (1, 1)
first_stage_anchor_generator = grid_anchor_generator.GridAnchorGenerator(
first_stage_anchor_scales,
first_stage_anchor_aspect_ratios,
anchor_stride=first_stage_anchor_strides)
first_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'proposal',
use_matmul_gather=use_matmul_gather_in_matcher)
if use_keras:
fake_feature_extractor = FakeFasterRCNNKerasFeatureExtractor()
else:
fake_feature_extractor = FakeFasterRCNNFeatureExtractor()
first_stage_box_predictor_hyperparams_text_proto = """
op: CONV
activation: RELU
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
}
}
"""
if use_keras:
first_stage_box_predictor_arg_scope_fn = (
self._build_keras_layer_hyperparams(
first_stage_box_predictor_hyperparams_text_proto))
else:
first_stage_box_predictor_arg_scope_fn = (
self._build_arg_scope_with_hyperparams(
first_stage_box_predictor_hyperparams_text_proto,
is_training))
first_stage_box_predictor_kernel_size = 3
first_stage_atrous_rate = 1
first_stage_box_predictor_depth = 512
first_stage_minibatch_size = 3
first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=0.5, is_static=use_static_shapes)
first_stage_nms_score_threshold = -1.0
first_stage_nms_iou_threshold = 1.0
first_stage_max_proposals = first_stage_max_proposals
first_stage_non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=first_stage_nms_score_threshold,
iou_thresh=first_stage_nms_iou_threshold,
max_size_per_class=first_stage_max_proposals,
max_total_size=first_stage_max_proposals,
use_static_shapes=use_static_shapes)
first_stage_localization_loss_weight = 1.0
first_stage_objectness_loss_weight = 1.0
post_processing_config = post_processing_pb2.PostProcessing()
post_processing_text_proto = """
score_converter: IDENTITY
batch_non_max_suppression {
score_threshold: -20.0
iou_threshold: 1.0
max_detections_per_class: 5
max_total_detections: 5
use_static_shapes: """ + '{}'.format(use_static_shapes) + """
}
"""
if calibration_mapping_value:
calibration_text_proto = """
calibration_config {
function_approximation {
x_y_pairs {
x_y_pair {
x: 0.0
y: %f
}
x_y_pair {
x: 1.0
y: %f
}}}}""" % (calibration_mapping_value, calibration_mapping_value)
post_processing_text_proto = (post_processing_text_proto + ' ' +
calibration_text_proto)
text_format.Merge(post_processing_text_proto, post_processing_config)
second_stage_non_max_suppression_fn, second_stage_score_conversion_fn = (
post_processing_builder.build(post_processing_config))
second_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'detection',
use_matmul_gather=use_matmul_gather_in_matcher)
second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=1.0, is_static=use_static_shapes)
second_stage_localization_loss_weight = 1.0
second_stage_classification_loss_weight = 1.0
if softmax_second_stage_classification_loss:
second_stage_classification_loss = (
losses.WeightedSoftmaxClassificationLoss())
else:
second_stage_classification_loss = (
losses.WeightedSigmoidClassificationLoss())
hard_example_miner = None
if hard_mining:
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=1,
iou_threshold=0.99,
loss_type='both',
cls_loss_weight=second_stage_classification_loss_weight,
loc_loss_weight=second_stage_localization_loss_weight,
max_negatives_per_positive=None)
crop_and_resize_fn = (ops.matmul_crop_and_resize
if use_matmul_crop_and_resize else
ops.native_crop_and_resize)
common_kwargs = {
'is_training':
is_training,
'num_classes':
num_classes,
'image_resizer_fn':
image_resizer_fn,
'feature_extractor':
fake_feature_extractor,
'number_of_stages':
number_of_stages,
'first_stage_anchor_generator':
first_stage_anchor_generator,
'first_stage_target_assigner':
first_stage_target_assigner,
'first_stage_atrous_rate':
first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope_fn':
first_stage_box_predictor_arg_scope_fn,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth':
first_stage_box_predictor_depth,
'first_stage_minibatch_size':
first_stage_minibatch_size,
'first_stage_sampler':
first_stage_sampler,
'first_stage_non_max_suppression_fn':
first_stage_non_max_suppression_fn,
'first_stage_max_proposals':
first_stage_max_proposals,
'first_stage_localization_loss_weight':
first_stage_localization_loss_weight,
'first_stage_objectness_loss_weight':
first_stage_objectness_loss_weight,
'second_stage_target_assigner':
second_stage_target_assigner,
'second_stage_batch_size':
second_stage_batch_size,
'second_stage_sampler':
second_stage_sampler,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn':
second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'second_stage_classification_loss':
second_stage_classification_loss,
'hard_example_miner':
hard_example_miner,
'crop_and_resize_fn':
crop_and_resize_fn,
'clip_anchors_to_image':
clip_anchors_to_image,
'use_static_shapes':
use_static_shapes,
'resize_masks':
True,
'return_raw_detections_during_predict':
return_raw_detections_during_predict
}
return self._get_model(
self._get_second_stage_box_predictor(
num_classes=num_classes,
is_training=is_training,
use_keras=use_keras,
predict_masks=predict_masks,
masks_are_class_agnostic=masks_are_class_agnostic,
share_box_across_classes=share_box_across_classes),
**common_kwargs)
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.meta_architectures import rfcn_meta_arch
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.predictors.heads import mask_head
from object_detection.protos import losses_pb2
from object_detection.protos import model_pb2
from object_detection.utils import label_map_util
from object_detection.utils import ops
from object_detection.utils import tf_version
## Feature Extractors for TF
## This section conditionally imports different feature extractors based on the
## Tensorflow version.
##
# pylint: disable=g-import-not-at-top
if tf_version.is_tf2():
from object_detection.models import center_net_hourglass_feature_extractor
from object_detection.models import center_net_mobilenet_v2_feature_extractor
from object_detection.models import center_net_resnet_feature_extractor
from object_detection.models import center_net_resnet_v1_fpn_feature_extractor
from object_detection.models import faster_rcnn_inception_resnet_v2_keras_feature_extractor as frcnn_inc_res_keras
from object_detection.models import faster_rcnn_resnet_keras_feature_extractor as frcnn_resnet_keras
from object_detection.models import ssd_resnet_v1_fpn_keras_feature_extractor as ssd_resnet_v1_fpn_keras
from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_resnet_fpn_keras
from object_detection.models.ssd_mobilenet_v1_fpn_keras_feature_extractor import SSDMobileNetV1FpnKerasFeatureExtractor
from object_detection.models.ssd_mobilenet_v1_keras_feature_extractor import SSDMobileNetV1KerasFeatureExtractor
from object_detection.models.ssd_mobilenet_v2_fpn_keras_feature_extractor import SSDMobileNetV2FpnKerasFeatureExtractor
from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
from object_detection.predictors import rfcn_keras_box_predictor
if sys.version_info[0] >= 3:
from object_detection.models import ssd_efficientnet_bifpn_feature_extractor as ssd_efficientnet_bifpn
class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
def _get_categories_list(self):
return [{
'id': 1,
'name': 'person'
}, {
'id': 2,
'name': 'dog'
}, {
'id': 3,
'name': 'cat'
}]
def _get_categories_list_with_keypoints(self):
return [{
'id': 1,
'name': 'person',
'keypoints': {
'left_eye': 0,
'right_eye': 3
}
}, {
'id': 2,
'name': 'dog',
'keypoints': {
'tail_start': 1,
'mouth': 2
}
}, {
'id': 3,
'name': 'cat'
}]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(i) for i in range(batch_size)])
detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
multiples=[batch_size, 1, 1])
detection_scores = tf.tile(tf.constant([[0.8]]),
multiples=[batch_size, 1])
detection_classes = tf.tile(tf.constant([[0]]),
multiples=[batch_size, 1])
detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20],
dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
num_detections = tf.ones([batch_size])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
groundtruth_keypoints = tf.constant([[0.0, 0.0], [0.5, 0.5],
[1.0, 1.0]])
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10],
dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(tf.expand_dims(
groundtruth_classes, 0),
multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
groundtruth_keypoints = tf.tile(tf.expand_dims(
groundtruth_keypoints, 0),
multiples=[batch_size, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes:
groundtruth_boxes,
input_data_fields.groundtruth_classes:
groundtruth_classes,
input_data_fields.groundtruth_keypoints:
groundtruth_keypoints,
input_data_fields.groundtruth_instance_masks:
groundtruth_instance_masks
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image,
key,
detections,
groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image,
key,
detections,
groundtruth,
scale_to_absolute=scale_to_absolute)
@parameterized.parameters(
{
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': True
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': True
}, {
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': False
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': False
})
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections(self,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(['coco_detection_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(
batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op = metric_ops['DetectionBoxes_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0,
metrics['DetectionBoxes_Precision/mAP'])
self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
@parameterized.parameters(
{
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': True
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': True
}, {
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': False
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': False
})
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections_and_masks(
self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'coco_mask_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(
batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
_, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op_boxes)
sess.run(update_op_masks)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0,
metrics['DetectionBoxes_Precision/mAP'])
self.assertAlmostEqual(1.0,
metrics['DetectionMasks_Precision/mAP'])
@parameterized.parameters(
{
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': True
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': True
}, {
'batch_size': 1,
'max_gt_boxes': None,
'scale_to_absolute': False
}, {
'batch_size': 8,
'max_gt_boxes': [1],
'scale_to_absolute': False
})
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'coco_mask_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(
batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute,
resized_groundtruth_masks=True)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
_, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op_boxes)
sess.run(update_op_masks)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0,
metrics['DetectionBoxes_Precision/mAP'])
self.assertAlmostEqual(1.0,
metrics['DetectionMasks_Precision/mAP'])
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(['unsupported_metric'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict()
with self.assertRaises(ValueError):
eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
def test_get_eval_metric_ops_for_evaluators(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend([
'coco_detection_metrics', 'coco_mask_metrics',
'precision_at_recall_detection_metrics'
])
eval_config.include_metrics_per_category = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
self.assertTrue(evaluator_options['coco_detection_metrics']
['include_metrics_per_category'])
self.assertTrue(evaluator_options['coco_mask_metrics']
['include_metrics_per_category'])
self.assertAlmostEqual(
evaluator_options['precision_at_recall_detection_metrics']
['recall_lower_bound'], eval_config.recall_lower_bound)
self.assertAlmostEqual(
evaluator_options['precision_at_recall_detection_metrics']
['recall_upper_bound'], eval_config.recall_upper_bound)
def test_get_evaluator_with_evaluator_options(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend([
'coco_detection_metrics', 'precision_at_recall_detection_metrics'
])
eval_config.include_metrics_per_category = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
categories = self._get_categories_list()
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
evaluator = eval_util.get_evaluators(eval_config, categories,
evaluator_options)
self.assertTrue(evaluator[0]._include_metrics_per_category)
self.assertAlmostEqual(evaluator[1]._recall_lower_bound,
eval_config.recall_lower_bound)
self.assertAlmostEqual(evaluator[1]._recall_upper_bound,
eval_config.recall_upper_bound)
def test_get_evaluator_with_no_evaluator_options(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend([
'coco_detection_metrics', 'precision_at_recall_detection_metrics'
])
eval_config.include_metrics_per_category = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
categories = self._get_categories_list()
evaluator = eval_util.get_evaluators(eval_config,
categories,
evaluator_options=None)
# Even though we are setting eval_config.include_metrics_per_category = True
# and bounds on recall, these options are never passed into the
# DetectionEvaluator constructor (via `evaluator_options`).
self.assertFalse(evaluator[0]._include_metrics_per_category)
self.assertAlmostEqual(evaluator[1]._recall_lower_bound, 0.0)
self.assertAlmostEqual(evaluator[1]._recall_upper_bound, 1.0)
def test_get_evaluator_with_keypoint_metrics(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'person'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'left_eye'] = 0.1
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'right_eye'] = 0.2
dog_keypoints_metric = eval_config.parameterized_metric.add()
dog_keypoints_metric.coco_keypoint_metrics.class_label = 'dog'
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'tail_start'] = 0.3
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'mouth'] = 0.4
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(eval_config,
categories,
evaluator_options=None)
# Verify keypoint evaluator class variables.
self.assertLen(evaluator, 3)
self.assertFalse(evaluator[0]._include_metrics_per_category)
self.assertEqual(evaluator[1]._category_name, 'person')
self.assertEqual(evaluator[2]._category_name, 'dog')
self.assertAllEqual(evaluator[1]._keypoint_ids, [0, 3])
self.assertAllEqual(evaluator[2]._keypoint_ids, [1, 2])
self.assertAllClose([0.1, 0.2], evaluator[1]._oks_sigmas)
self.assertAllClose([0.3, 0.4], evaluator[2]._oks_sigmas)
def test_get_evaluator_with_unmatched_label(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'unmatched'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'kpt'] = 0.1
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(eval_config,
categories,
evaluator_options=None)
self.assertLen(evaluator, 1)
self.assertNotIsInstance(evaluator[0],
coco_evaluation.CocoKeypointEvaluator)
def test_padded_image_result_dict(self):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
key = tf.constant([str(i) for i in range(2)])
detection_boxes = np.array(
[[[0., 0., 1., 1.]], [[0.0, 0.0, 0.5, 0.5]]], dtype=np.float32)
detection_keypoints = np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]],
dtype=np.float32)
def graph_fn():
detections = {
detection_fields.detection_boxes:
tf.constant(detection_boxes),
detection_fields.detection_scores:
tf.constant([[1.], [1.]]),
detection_fields.detection_classes:
tf.constant([[1], [2]]),
detection_fields.num_detections:
tf.constant([1, 1]),
detection_fields.detection_keypoints:
tf.tile(tf.reshape(tf.constant(detection_keypoints),
shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
gt_boxes = detection_boxes
groundtruth = {
input_data_fields.groundtruth_boxes:
tf.constant(gt_boxes),
input_data_fields.groundtruth_classes:
tf.constant([[1.], [1.]]),
input_data_fields.groundtruth_keypoints:
tf.tile(tf.reshape(tf.constant(detection_keypoints),
shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
image = tf.zeros((2, 100, 100, 3), dtype=tf.float32)
true_image_shapes = tf.constant([[100, 100, 3], [50, 100, 3]])
original_image_spatial_shapes = tf.constant([[200, 200],
[150, 300]])
result = eval_util.result_dict_for_batched_example(
image,
key,
detections,
groundtruth,
scale_to_absolute=True,
true_image_shapes=true_image_shapes,
original_image_spatial_shapes=original_image_spatial_shapes,
max_gt_boxes=tf.constant(1))
return (result[input_data_fields.groundtruth_boxes],
result[input_data_fields.groundtruth_keypoints],
result[detection_fields.detection_boxes],
result[detection_fields.detection_keypoints])
(gt_boxes, gt_keypoints, detection_boxes,
detection_keypoints) = self.execute_cpu(graph_fn, [])
self.assertAllEqual([[[0., 0., 200., 200.]], [[0.0, 0.0, 150., 150.]]],
gt_boxes)
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [150., 150.], [300., 300.]]]],
gt_keypoints)
# Predictions from the model are not scaled.
self.assertAllEqual([[[0., 0., 200., 200.]], [[0.0, 0.0, 75., 150.]]],
detection_boxes)
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [75., 150.], [150., 300.]]]],
detection_keypoints)
def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
"""Builds a Faster R-CNN or R-FCN detection model based on the model config.
Builds R-FCN model if the second_stage_box_predictor in the config is of type
`rfcn_box_predictor` else builds a Faster R-CNN model.
Args:
frcnn_config: A faster_rcnn.proto object containing the config for the
desired FasterRCNNMetaArch or RFCNMetaArch.
is_training: True if this model is being built for training purposes.
add_summaries: Whether to add tf summaries in the model.
Returns:
FasterRCNNMetaArch based on the config.
Raises:
ValueError: If frcnn_config.type is not recognized (i.e. not registered in
model_class_map).
"""
num_classes = frcnn_config.num_classes
image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
_check_feature_extractor_exists(frcnn_config.feature_extractor.type)
is_keras = tf_version.is_tf2()
if is_keras:
feature_extractor = _build_faster_rcnn_keras_feature_extractor(
frcnn_config.feature_extractor,
is_training,
inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)
else:
feature_extractor = _build_faster_rcnn_feature_extractor(
frcnn_config.feature_extractor,
is_training,
inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)
number_of_stages = frcnn_config.number_of_stages
first_stage_anchor_generator = anchor_generator_builder.build(
frcnn_config.first_stage_anchor_generator)
first_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'proposal',
use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
if is_keras:
first_stage_box_predictor_arg_scope_fn = (
hyperparams_builder.KerasLayerHyperparams(
frcnn_config.first_stage_box_predictor_conv_hyperparams))
else:
first_stage_box_predictor_arg_scope_fn = hyperparams_builder.build(
frcnn_config.first_stage_box_predictor_conv_hyperparams,
is_training)
first_stage_box_predictor_kernel_size = (
frcnn_config.first_stage_box_predictor_kernel_size)
first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
use_static_shapes = frcnn_config.use_static_shapes and (
frcnn_config.use_static_shapes_for_eval or is_training)
first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=frcnn_config.first_stage_positive_balance_fraction,
is_static=(frcnn_config.use_static_balanced_label_sampler
and use_static_shapes))
first_stage_max_proposals = frcnn_config.first_stage_max_proposals
if (frcnn_config.first_stage_nms_iou_threshold < 0
or frcnn_config.first_stage_nms_iou_threshold > 1.0):
raise ValueError('iou_threshold not in [0, 1.0].')
if (is_training and
frcnn_config.second_stage_batch_size > first_stage_max_proposals):
raise ValueError('second_stage_batch_size should be no greater than '
'first_stage_max_proposals.')
first_stage_non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=frcnn_config.first_stage_nms_score_threshold,
iou_thresh=frcnn_config.first_stage_nms_iou_threshold,
max_size_per_class=frcnn_config.first_stage_max_proposals,
max_total_size=frcnn_config.first_stage_max_proposals,
use_static_shapes=use_static_shapes,
use_partitioned_nms=frcnn_config.use_partitioned_nms_in_first_stage,
use_combined_nms=frcnn_config.use_combined_nms_in_first_stage)
first_stage_loc_loss_weight = (
frcnn_config.first_stage_localization_loss_weight)
first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
initial_crop_size = frcnn_config.initial_crop_size
maxpool_kernel_size = frcnn_config.maxpool_kernel_size
maxpool_stride = frcnn_config.maxpool_stride
second_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'detection',
use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
if is_keras:
second_stage_box_predictor = box_predictor_builder.build_keras(
hyperparams_builder.KerasLayerHyperparams,
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[1],
box_predictor_config=frcnn_config.second_stage_box_predictor,
is_training=is_training,
num_classes=num_classes)
else:
second_stage_box_predictor = box_predictor_builder.build(
hyperparams_builder.build,
frcnn_config.second_stage_box_predictor,
is_training=is_training,
num_classes=num_classes)
second_stage_batch_size = frcnn_config.second_stage_batch_size
second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=frcnn_config.second_stage_balance_fraction,
is_static=(frcnn_config.use_static_balanced_label_sampler
and use_static_shapes))
(second_stage_non_max_suppression_fn,
second_stage_score_conversion_fn) = post_processing_builder.build(
frcnn_config.second_stage_post_processing)
second_stage_localization_loss_weight = (
frcnn_config.second_stage_localization_loss_weight)
second_stage_classification_loss = (
losses_builder.build_faster_rcnn_classification_loss(
frcnn_config.second_stage_classification_loss))
second_stage_classification_loss_weight = (
frcnn_config.second_stage_classification_loss_weight)
second_stage_mask_prediction_loss_weight = (
frcnn_config.second_stage_mask_prediction_loss_weight)
hard_example_miner = None
if frcnn_config.HasField('hard_example_miner'):
hard_example_miner = losses_builder.build_hard_example_miner(
frcnn_config.hard_example_miner,
second_stage_classification_loss_weight,
second_stage_localization_loss_weight)
crop_and_resize_fn = (ops.matmul_crop_and_resize
if frcnn_config.use_matmul_crop_and_resize else
ops.native_crop_and_resize)
clip_anchors_to_image = (frcnn_config.clip_anchors_to_image)
common_kwargs = {
'is_training': is_training,
'num_classes': num_classes,
'image_resizer_fn': image_resizer_fn,
'feature_extractor': feature_extractor,
'number_of_stages': number_of_stages,
'first_stage_anchor_generator': first_stage_anchor_generator,
'first_stage_target_assigner': first_stage_target_assigner,
'first_stage_atrous_rate': first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope_fn':
first_stage_box_predictor_arg_scope_fn,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
'first_stage_minibatch_size': first_stage_minibatch_size,
'first_stage_sampler': first_stage_sampler,
'first_stage_non_max_suppression_fn':
first_stage_non_max_suppression_fn,
'first_stage_max_proposals': first_stage_max_proposals,
'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
'second_stage_target_assigner': second_stage_target_assigner,
'second_stage_batch_size': second_stage_batch_size,
'second_stage_sampler': second_stage_sampler,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss': second_stage_classification_loss,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'hard_example_miner': hard_example_miner,
'add_summaries': add_summaries,
'crop_and_resize_fn': crop_and_resize_fn,
'clip_anchors_to_image': clip_anchors_to_image,
'use_static_shapes': use_static_shapes,
'resize_masks': frcnn_config.resize_masks,
'return_raw_detections_during_predict':
frcnn_config.return_raw_detections_during_predict,
'output_final_box_features': frcnn_config.output_final_box_features
}
if ((not is_keras and isinstance(second_stage_box_predictor,
rfcn_box_predictor.RfcnBoxPredictor)) or
(is_keras
and isinstance(second_stage_box_predictor,
rfcn_keras_box_predictor.RfcnKerasBoxPredictor))):
return rfcn_meta_arch.RFCNMetaArch(
second_stage_rfcn_box_predictor=second_stage_box_predictor,
**common_kwargs)
elif frcnn_config.HasField('context_config'):
context_config = frcnn_config.context_config
common_kwargs.update({
'attention_bottleneck_dimension':
context_config.attention_bottleneck_dimension,
'attention_temperature':
context_config.attention_temperature
})
return context_rcnn_meta_arch.ContextRCNNMetaArch(
initial_crop_size=initial_crop_size,
maxpool_kernel_size=maxpool_kernel_size,
maxpool_stride=maxpool_stride,
second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
second_stage_mask_prediction_loss_weight=(
second_stage_mask_prediction_loss_weight),
**common_kwargs)
else:
return faster_rcnn_meta_arch.FasterRCNNMetaArch(
initial_crop_size=initial_crop_size,
maxpool_kernel_size=maxpool_kernel_size,
maxpool_stride=maxpool_stride,
second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
second_stage_mask_prediction_loss_weight=(
second_stage_mask_prediction_loss_weight),
**common_kwargs)
class ConfigUtilTest(tf.test.TestCase):
def _create_and_load_test_configs(self, pipeline_config):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
return config_util.get_configs_from_pipeline_file(pipeline_config_path)
def test_get_configs_from_pipeline_file(self):
"""Test that proto configs can be read from pipeline config file."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config,
configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_configs"])
def test_create_configs_from_pipeline_proto(self):
"""Tests creating configs dictionary from pipeline proto."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
configs = config_util.create_configs_from_pipeline_proto(pipeline_config)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config, configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_configs"])
def test_create_pipeline_proto_from_configs(self):
"""Tests that proto can be reconstructed from configs dictionary."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def test_save_pipeline_config(self):
"""Tests that the pipeline config is properly saved to disk."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
configs = config_util.get_configs_from_pipeline_file(
os.path.join(self.get_temp_dir(), "pipeline.config"))
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
def _assertOptimizerWithNewLearningRate(self, optimizer_name):
"""Asserts successful updating of all learning rate schemes."""
original_learning_rate = 0.7
learning_rate_scaling = 0.1
warmup_learning_rate = 0.07
hparams = contrib_training.HParams(learning_rate=0.15)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
# Constant learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_constant_learning_rate(optimizer,
original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
constant_lr = optimizer.learning_rate.constant_learning_rate
self.assertAlmostEqual(hparams.learning_rate, constant_lr.learning_rate)
# Exponential decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_exponential_decay_learning_rate(
optimizer, original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
exponential_lr.initial_learning_rate)
# Manual step learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_manual_step_learning_rate(
optimizer, original_learning_rate, learning_rate_scaling)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
manual_lr = optimizer.learning_rate.manual_step_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
manual_lr.initial_learning_rate)
for i, schedule in enumerate(manual_lr.schedule):
self.assertAlmostEqual(hparams.learning_rate * learning_rate_scaling**i,
schedule.learning_rate)
# Cosine decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_cosine_decay_learning_rate(optimizer,
original_learning_rate,
warmup_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate, cosine_lr.learning_rate_base)
warmup_scale_factor = warmup_learning_rate / original_learning_rate
self.assertAlmostEqual(hparams.learning_rate * warmup_scale_factor,
cosine_lr.warmup_learning_rate)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testRMSPropWithNewLearingRate(self):
"""Tests new learning rates for RMSProp Optimizer."""
self._assertOptimizerWithNewLearningRate("rms_prop_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testMomentumOptimizerWithNewLearningRate(self):
"""Tests new learning rates for Momentum Optimizer."""
self._assertOptimizerWithNewLearningRate("momentum_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testAdamOptimizerWithNewLearningRate(self):
"""Tests new learning rates for Adam Optimizer."""
self._assertOptimizerWithNewLearningRate("adam_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testGenericConfigOverride(self):
"""Tests generic config overrides for all top-level configs."""
# Set one parameter for each of the top-level pipeline configs:
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.num_classes = 1
pipeline_config.train_config.batch_size = 1
pipeline_config.eval_config.num_visualizations = 1
pipeline_config.train_input_reader.label_map_path = "/some/path"
pipeline_config.eval_input_reader.add().label_map_path = "/some/path"
pipeline_config.graph_rewriter.quantization.weight_bits = 1
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Override each of the parameters:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(
**{
"model.ssd.num_classes": 2,
"train_config.batch_size": 2,
"train_input_config.label_map_path": "/some/other/path",
"eval_config.num_visualizations": 2,
"graph_rewriter_config.quantization.weight_bits": 2
})
configs = config_util.merge_external_params_with_configs(configs, hparams)
# Ensure that the parameters have the overridden values:
self.assertEqual(2, configs["model"].ssd.num_classes)
self.assertEqual(2, configs["train_config"].batch_size)
self.assertEqual("/some/other/path",
configs["train_input_config"].label_map_path)
self.assertEqual(2, configs["eval_config"].num_visualizations)
self.assertEqual(2,
configs["graph_rewriter_config"].quantization.weight_bits)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewBatchSize(self):
"""Tests that batch size is updated appropriately."""
original_batch_size = 2
hparams = contrib_training.HParams(batch_size=16)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(16, new_batch_size)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewBatchSizeWithClipping(self):
"""Tests that batch size is clipped to 1 from below."""
original_batch_size = 2
hparams = contrib_training.HParams(batch_size=0.5)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(1, new_batch_size) # Clipped to 1.0.
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteBatchSizeWithKeyValue(self):
"""Tests that batch size is overwritten based on key/value."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
hparams = contrib_training.HParams(**{"train_config.batch_size": 10})
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(10, new_batch_size)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteSampleFromDatasetWeights(self):
"""Tests config override for sample_from_datasets_weights."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.sample_from_datasets_weights.extend(
[1, 2])
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Override parameters:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(sample_from_datasets_weights=[0.5, 0.5])
configs = config_util.merge_external_params_with_configs(configs, hparams)
# Ensure that the parameters have the overridden values:
self.assertListEqual(
[0.5, 0.5],
list(configs["train_input_config"].sample_from_datasets_weights))
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteSampleFromDatasetWeightsWrongLength(self):
"""Tests config override for sample_from_datasets_weights."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.sample_from_datasets_weights.extend(
[1, 2])
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Try to override parameter with too many weights:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(
sample_from_datasets_weights=[0.5, 0.5, 0.5])
with self.assertRaises(
ValueError,
msg="sample_from_datasets_weights override has a different number of"
" values (3) than the configured dataset weights (2)."
):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testKeyValueOverrideBadKey(self):
"""Tests that overwriting with a bad key causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
configs = self._create_and_load_test_configs(pipeline_config)
hparams = contrib_training.HParams(**{"train_config.no_such_field": 10})
with self.assertRaises(ValueError):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteBatchSizeWithBadValueType(self):
"""Tests that overwriting with a bad valuye type causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
# Type should be an integer, but we're passing a string "10".
hparams = contrib_training.HParams(**{"train_config.batch_size": "10"})
with self.assertRaises(TypeError):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewMomentumOptimizerValue(self):
"""Tests that new momentum value is updated appropriately."""
original_momentum_value = 0.4
hparams = contrib_training.HParams(momentum_optimizer_value=1.1)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
optimizer_config.momentum_optimizer_value = original_momentum_value
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
new_momentum_value = optimizer_config.momentum_optimizer_value
self.assertAlmostEqual(1.0, new_momentum_value) # Clipped to 1.0.
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewClassificationLocalizationWeightRatio(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_localization_weight = 0.1
original_classification_weight = 0.2
new_weight_ratio = 5.0
hparams = contrib_training.HParams(
classification_localization_weight_ratio=new_weight_ratio)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.loss.localization_weight = (
original_localization_weight)
pipeline_config.model.ssd.loss.classification_weight = (
original_classification_weight)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
loss = configs["model"].ssd.loss
self.assertAlmostEqual(1.0, loss.localization_weight)
self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewFocalLossParameters(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_alpha = 1.0
original_gamma = 1.0
new_alpha = 0.3
new_gamma = 2.0
hparams = contrib_training.HParams(
focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
classification_loss = pipeline_config.model.ssd.loss.classification_loss
classification_loss.weighted_sigmoid_focal.alpha = original_alpha
classification_loss.weighted_sigmoid_focal.gamma = original_gamma
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
classification_loss = configs["model"].ssd.loss.classification_loss
self.assertAlmostEqual(new_alpha,
classification_loss.weighted_sigmoid_focal.alpha)
self.assertAlmostEqual(new_gamma,
classification_loss.weighted_sigmoid_focal.gamma)
def testMergingKeywordArguments(self):
"""Tests that keyword arguments get merged as do hyperparameters."""
original_num_train_steps = 100
desired_num_train_steps = 10
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.num_steps = original_num_train_steps
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_steps": desired_num_train_steps}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_steps = configs["train_config"].num_steps
self.assertEqual(desired_num_train_steps, train_steps)
def testGetNumberOfClasses(self):
"""Tests that number of classes can be retrieved."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 20
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
number_of_classes = config_util.get_number_of_classes(configs["model"])
self.assertEqual(20, number_of_classes)
def testNewTrainInputPath(self):
"""Tests that train input path can be overwritten with single file."""
original_train_path = ["path/to/data"]
new_train_path = "another/path/to/data"
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
reader_config = pipeline_config.train_input_reader.tf_record_input_reader
reader_config.input_path.extend(original_train_path)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_input_path": new_train_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual([new_train_path], final_path)
def testNewTrainInputPathList(self):
"""Tests that train input path can be overwritten with multiple files."""
original_train_path = ["path/to/data"]
new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
reader_config = pipeline_config.train_input_reader.tf_record_input_reader
reader_config.input_path.extend(original_train_path)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_input_path": new_train_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual(new_train_path, final_path)
def testNewLabelMapPath(self):
"""Tests that label map path can be overwritten in input readers."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = "path//to/new/label_map"
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.label_map_path = original_label_map_path
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.label_map_path = original_label_map_path
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_label_map_path,
configs["train_input_config"].label_map_path)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_label_map_path, eval_input_config.label_map_path)
def testDontOverwriteEmptyLabelMapPath(self):
"""Tests that label map path will not by overwritten with empty string."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = ""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.label_map_path = original_label_map_path
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.label_map_path = original_label_map_path
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(original_label_map_path,
configs["train_input_config"].label_map_path)
self.assertEqual(original_label_map_path,
configs["eval_input_configs"][0].label_map_path)
def testNewMaskType(self):
"""Tests that mask type can be overwritten in input readers."""
original_mask_type = input_reader_pb2.NUMERICAL_MASKS
new_mask_type = input_reader_pb2.PNG_MASKS
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.mask_type = original_mask_type
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.mask_type = original_mask_type
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
self.assertEqual(new_mask_type, configs["eval_input_configs"][0].mask_type)
def testUseMovingAverageForEval(self):
use_moving_averages_orig = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = use_moving_averages_orig
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_with_moving_averages": True}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(True, configs["eval_config"].use_moving_averages)
def testGetImageResizerConfig(self):
"""Tests that number of classes can be retrieved."""
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
image_resizer_config = config_util.get_image_resizer_config(model_config)
self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
def testGetSpatialImageSizeFromFixedShapeResizerConfig(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.fixed_shape_resizer.height = 100
image_resizer_config.fixed_shape_resizer.width = 200
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [100, 200])
def testGetSpatialImageSizeFromAspectPreservingResizerConfig(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension = True
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [600, 600])
def testGetSpatialImageSizeFromAspectPreservingResizerDynamic(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
def testGetSpatialImageSizeFromConditionalShapeResizer(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.conditional_shape_resizer.size_threshold = 100
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
def testGetMaxNumContextFeaturesFromModelConfig(self):
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.context_config.max_num_context_features = 10
max_num_context_features = config_util.get_max_num_context_features(
model_config)
self.assertAllEqual(max_num_context_features, 10)
def testGetContextFeatureLengthFromModelConfig(self):
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.context_config.context_feature_length = 100
context_feature_length = config_util.get_context_feature_length(
model_config)
self.assertAllEqual(context_feature_length, 100)
def testEvalShuffle(self):
"""Tests that `eval_shuffle` keyword arguments are applied correctly."""
original_shuffle = True
desired_shuffle = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_shuffle": desired_shuffle}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(desired_shuffle, configs["eval_input_configs"][0].shuffle)
def testTrainShuffle(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_shuffle = True
desired_shuffle = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_shuffle": desired_shuffle}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_shuffle = configs["train_input_config"].shuffle
self.assertEqual(desired_shuffle, train_shuffle)
def testOverWriteRetainOriginalImages(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_retain_original_images = True
desired_retain_original_images = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.retain_original_images = (
original_retain_original_images)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {
"retain_original_images_in_eval": desired_retain_original_images
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
retain_original_images = configs["eval_config"].retain_original_images
self.assertEqual(desired_retain_original_images, retain_original_images)
def testOverwriteAllEvalSampling(self):
original_num_eval_examples = 1
new_num_eval_examples = 10
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"sample_1_of_n_eval_examples": new_num_eval_examples}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_eval_examples,
eval_input_config.sample_1_of_n_examples)
def testOverwriteAllEvalNumEpochs(self):
original_num_epochs = 10
new_num_epochs = 1
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_num_epochs": new_num_epochs}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_epochs, eval_input_config.num_epochs)
def testUpdateMaskTypeForAllInputConfigs(self):
original_mask_type = input_reader_pb2.NUMERICAL_MASKS
new_mask_type = input_reader_pb2.PNG_MASKS
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_config = pipeline_config.train_input_reader
train_config.mask_type = original_mask_type
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.mask_type = original_mask_type
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.mask_type = original_mask_type
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(configs["train_input_config"].mask_type, new_mask_type)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(eval_input_config.mask_type, new_mask_type)
def testErrorOverwritingMultipleInputConfig(self):
original_shuffle = False
new_shuffle = True
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.shuffle = original_shuffle
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.shuffle = original_shuffle
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_shuffle": new_shuffle}
with self.assertRaises(ValueError):
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
def testCheckAndParseInputConfigKey(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "eval_1"
pipeline_config.eval_input_reader.add().name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
specific_shuffle_update_key = "eval_input_configs:eval_2:shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, specific_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, "eval_2")
self.assertEqual(field_name, "shuffle")
legacy_shuffle_update_key = "eval_shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(configs,
legacy_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, None)
self.assertEqual(field_name, "shuffle")
non_input_config_update_key = "label_map_path"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, non_input_config_update_key))
self.assertFalse(is_valid_input_config_key)
self.assertEqual(key_name, None)
self.assertEqual(input_name, None)
self.assertEqual(field_name, "label_map_path")
with self.assertRaisesRegexp(ValueError,
"Invalid key format when overriding configs."):
config_util.check_and_parse_input_config_key(
configs, "train_input_config:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid key_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "invalid_key_name:train_name:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid input_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:unknown_eval_name:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid field_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:eval_2:unknown_field_name")
def testUpdateInputReaderConfigSuccess(self):
original_shuffle = False
new_shuffle = True
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
config_util.update_input_reader_config(
configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
config_util.update_input_reader_config(
configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
def testUpdateInputReaderConfigErrors(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "same_eval_name"
pipeline_config.eval_input_reader.add().name = "same_eval_name"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
with self.assertRaisesRegexp(ValueError,
"Duplicate input name found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="same_eval_name",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(
ValueError, "Input name name_not_exist not found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="name_not_exist",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(ValueError,
"Unknown input config overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name=None,
field_name="shuffle",
value=False)
def testOverWriteRetainOriginalImageAdditionalChannels(self):
"""Tests that keyword arguments are applied correctly."""
original_retain_original_image_additional_channels = True
desired_retain_original_image_additional_channels = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.retain_original_image_additional_channels = (
original_retain_original_image_additional_channels)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {
"retain_original_image_additional_channels_in_eval":
desired_retain_original_image_additional_channels
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
retain_original_image_additional_channels = configs[
"eval_config"].retain_original_image_additional_channels
self.assertEqual(desired_retain_original_image_additional_channels,
retain_original_image_additional_channels)
def testUpdateNumClasses(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
self.assertEqual(config_util.get_number_of_classes(configs["model"]), 10)
config_util.merge_external_params_with_configs(
configs, kwargs_dict={"num_classes": 2})
self.assertEqual(config_util.get_number_of_classes(configs["model"]), 2)
def testRemoveUnecessaryEma(self):
input_dict = {
"expanded_conv_10/project/act_quant/min":
1,
"FeatureExtractor/MobilenetV2_2/expanded_conv_5/expand/act_quant/min":
2,
"expanded_conv_10/expand/BatchNorm/gamma/min/ExponentialMovingAverage":
3,
"expanded_conv_3/depthwise/BatchNorm/beta/max/ExponentialMovingAverage":
4,
"BoxPredictor_1/ClassPredictor_depthwise/act_quant":
5
}
no_ema_collection = ["/min", "/max"]
output_dict = {
"expanded_conv_10/project/act_quant/min":
1,
"FeatureExtractor/MobilenetV2_2/expanded_conv_5/expand/act_quant/min":
2,
"expanded_conv_10/expand/BatchNorm/gamma/min":
3,
"expanded_conv_3/depthwise/BatchNorm/beta/max":
4,
"BoxPredictor_1/ClassPredictor_depthwise/act_quant":
5
}
self.assertEqual(
output_dict,
config_util.remove_unecessary_ema(input_dict, no_ema_collection))