def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
optimize_graph=True,
output_collection_name='inference_op',
additional_output_tensor_names=None):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of [`image_tensor`,
`tf_example`].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
optimize_graph: Whether to optimize graph using Grappler.
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
_export_inference_graph(input_type, detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix, output_directory,
additional_output_tensor_names, input_shape,
optimize_graph, output_collection_name)
def test_create_faster_rcnn_model_from_config_with_example_miner(self):
model_text_proto = """
faster_rcnn {
num_classes: 3
feature_extractor {
type: 'faster_rcnn_inception_resnet_v2'
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
second_stage_box_predictor {
mask_rcnn_box_predictor {
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
hard_example_miner {
num_hard_examples: 10
iou_threshold: 0.99
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
model = model_builder.build(model_proto, is_training=True)
self.assertIsNotNone(model._hard_example_miner)
def create_model(self, model_config):
"""Builds a DetectionModel based on the model config.
Args:
model_config: A model.proto object containing the config for the desired
DetectionModel.
Returns:
DetectionModel based on the config.
"""
return model_builder.build(model_config, is_training=True)
def test_create_rfcn_resnet_v1_model_from_config(self):
model_text_proto = """
faster_rcnn {
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet101'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
rfcn_box_predictor {
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.01
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
for extractor_type, extractor_class in FEATURE_EXTRACTOR_MAPS.items():
model_proto.faster_rcnn.feature_extractor.type = extractor_type
model = model_builder.build(model_proto, is_training=True)
self.assertIsInstance(model, rfcn_meta_arch.RFCNMetaArch)
self.assertIsInstance(model._feature_extractor, extractor_class)
def test_create_faster_rcnn_inception_v2_model_from_config(self):
model_text_proto = """
faster_rcnn {
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_v2'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.01
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
model = model_builder.build(model_proto, is_training=True)
self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
self.assertIsInstance(
model._feature_extractor,
frcnn_inc_v2.FasterRCNNInceptionV2FeatureExtractor)
def test_create_faster_rcnn_resnet101_with_mask_prediction_enabled(self):
model_text_proto = """
faster_rcnn {
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet101'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
predict_instance_masks: true
}
}
second_stage_mask_prediction_loss_weight: 3.0
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.01
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
model = model_builder.build(model_proto, is_training=True)
self.assertAlmostEqual(model._second_stage_mask_loss_weight, 3.0)