def _create_bert_tokenizer(self):
vocab = _metadata_fb.AssociatedFileT()
vocab.name = os.path.basename(self.model_info.vocab_file)
vocab.description = "Vocabulary file for the BertTokenizer."
vocab.type = _metadata_fb.AssociatedFileType.VOCABULARY
tokenizer = _metadata_fb.ProcessUnitT()
tokenizer.optionsType = _metadata_fb.ProcessUnitOptions.BertTokenizerOptions
tokenizer.options = _metadata_fb.BertTokenizerOptionsT()
tokenizer.options.vocabFile = [vocab]
return tokenizer
def _create_sentence_piece_tokenizer(self):
sp_model = _metadata_fb.AssociatedFileT()
sp_model.name = os.path.basename(self.model_info.sp_model)
sp_model.description = "The sentence piece model file."
if self.model_info.vocab_file is not None:
vocab = _metadata_fb.AssociatedFileT()
vocab.name = os.path.basename(self.model_info.vocab_file)
vocab.description = (
"Vocabulary file for the SentencePiece tokenizer. This file is "
"optional during tokenization, while the sentence piece model is "
"mandatory.")
vocab.type = _metadata_fb.AssociatedFileType.VOCABULARY
tokenizer = _metadata_fb.ProcessUnitT()
tokenizer.optionsType = (
_metadata_fb.ProcessUnitOptions.SentencePieceTokenizerOptions)
tokenizer.options = _metadata_fb.SentencePieceTokenizerOptionsT()
tokenizer.options.sentencePieceModel = [sp_model]
if vocab:
tokenizer.options.vocabFile = [vocab]
return tokenizer
def _write_metadata(model_path: str, label_map_path: str, mean: List[float],
std: List[float]):
"""Add normalization option and label map TFLite metadata to the model.
Args:
model_path: The path of the TFLite model
label_map_path: The path of the label map file
mean: The mean value used to normalize input image tensor
std: The standard deviation used to normalize input image tensor
"""
# Creates flatbuffer for model information.
model_meta = _metadata_fb.ModelMetadataT()
# Creates flatbuffer for model input metadata.
# Here we add the input normalization info to input metadata.
input_meta = _metadata_fb.TensorMetadataT()
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = mean
input_normalization.options.std = std
input_meta.processUnits = [input_normalization]
# Creates flatbuffer for model output metadata.
# Here we add label file to output metadata.
output_meta = _metadata_fb.TensorMetadataT()
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(label_map_path)
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
output_meta.associatedFiles = [label_file]
# Creates subgraph to contain input and output information,
# and add subgraph to the model information.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output_meta]
model_meta.subgraphMetadata = [subgraph]
# Serialize the model metadata buffer we created above using flatbuffer
# builder.
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
# Populates metadata and label file to the model file.
populator = _metadata.MetadataPopulator.with_model_file(model_path)
populator.load_metadata_buffer(metadata_buf)
populator.load_associated_files([label_map_path])
populator.populate()
def _create_output_metadata(self):
"""Creates the output metadata for a Bert text classifier model."""
# Create output info.
output_meta = _metadata_fb.TensorMetadataT()
output_meta.name = "probability"
output_meta.description = "Probabilities of labels respectively."
output_meta.content = _metadata_fb.ContentT()
output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT(
)
output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.model_info.label_file)
label_file.description = ("Labels for classification categories.")
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
output_meta.associatedFiles = [label_file]
return [output_meta]
def _create_metadata(self):
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = ("Identify the most prominent breed of a dog in the "
"image from a set of %d breeds." %
self.model_info.num_classes)
model_meta.version = self.model_info.version
model_meta.author = "https://github.com/puntogris/"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"Input image to be classified. The expected image is {0} x {1}, with "
"three channels (red, blue, and green) per pixel. Each value in the "
"tensor is a single byte between {2} and {3}.".format(
self.model_info.image_width, self.model_info.image_height,
self.model_info.image_min, self.model_info.image_max))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates output info.
output_meta = _metadata_fb.TensorMetadataT()
output_meta.name = "probability"
output_meta.description = "Probabilities of the %d labels respectively." % self.model_info.num_classes
output_meta.content = _metadata_fb.ContentT()
output_meta.content.content_properties = _metadata_fb.FeaturePropertiesT()
output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_stats = _metadata_fb.StatsT()
output_stats.max = [1.0]
output_stats.min = [0.0]
output_meta.stats = output_stats
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.label_file_path)
label_file.description = "Labels for objects that the model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
output_meta.associatedFiles = [label_file]
# Creates subgraph info.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output_meta]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(
model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
def _create_metadata(self):
"""Creates the metadata for an object detector."""
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = (
"Identify which of a known set of objects might be present and provide "
"information about their positions within the given image or a video "
"stream.")
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow Lite Model Maker"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"Input image to be detected. The expected image is {0} x {1}, with "
"three channels (red, blue, and green) per pixel. Each value in the "
"tensor is a single byte between 0 and 255.".format(
self.model_info.image_width, self.model_info.image_height))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates outputs info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 2
output_location_meta.content.range.max = 2
output_class_meta = _metadata_fb.TensorMetadataT()
output_class_meta.name = "category"
output_class_meta.description = "The categories of the detected boxes."
output_class_meta.content = _metadata_fb.ContentT()
output_class_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_class_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_class_meta.content.range = _metadata_fb.ValueRangeT()
output_class_meta.content.range.min = 2
output_class_meta.content.range.max = 2
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.associated_files[0])
label_file.description = "Label of objects that this model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
output_class_meta.associatedFiles = [label_file]
output_score_meta = _metadata_fb.TensorMetadataT()
output_score_meta.name = "score"
output_score_meta.description = "The scores of the detected boxes."
output_score_meta.content = _metadata_fb.ContentT()
output_score_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_score_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_score_meta.content.range = _metadata_fb.ValueRangeT()
output_score_meta.content.range.min = 2
output_score_meta.content.range.max = 2
output_number_meta = _metadata_fb.TensorMetadataT()
output_number_meta.name = "number of detections"
output_number_meta.description = "The number of the detected boxes."
output_number_meta.content = _metadata_fb.ContentT()
output_number_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_number_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
# Creates subgraph info.
group = _metadata_fb.TensorGroupT()
group.name = "detection result"
group.tensorNames = [
output_location_meta.name, output_class_meta.name,
output_score_meta.name
]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
output_location_meta, output_class_meta, output_score_meta,
output_number_meta
]
subgraph.outputTensorGroups = [group]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
def _create_metadata(self):
"""Creates the metadata for a text classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = self.model_info.description
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "input_text"
input_meta.description = (
"Embedding vectors representing the input text to be classified. The "
"input need to be converted from raw text to embedding vectors using "
"the attached dictionary file.")
# Create the vocab file.
vocab_file = _metadata_fb.AssociatedFileT()
vocab_file.name = os.path.basename(self.associated_files[1])
vocab_file.description = ("Vocabulary file to convert natural language "
"words to embedding vectors.")
vocab_file.type = _metadata_fb.AssociatedFileType.VOCABULARY
# Create the RegexTokenizer.
tokenizer = _metadata_fb.ProcessUnitT()
tokenizer.optionsType = (
_metadata_fb.ProcessUnitOptions.RegexTokenizerOptions)
tokenizer.options = _metadata_fb.RegexTokenizerOptionsT()
tokenizer.options.delimRegexPattern = self.model_info.delim_regex_pattern
tokenizer.options.vocabFile = [vocab_file]
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
input_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()
input_meta.processUnits = [tokenizer]
# Creates output info.
output_meta = _metadata_fb.TensorMetadataT()
output_meta.name = "probability"
output_meta.description = "Probabilities of the labels respectively."
output_meta.content = _metadata_fb.ContentT()
output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()
output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_stats = _metadata_fb.StatsT()
output_stats.max = [1.0]
output_stats.min = [0.0]
output_meta.stats = output_stats
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.associated_files[0])
label_file.description = ("Labels for the categories that the model can "
"classify.")
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
output_meta.associatedFiles = [label_file]
# Creates subgraph info.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output_meta]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(
model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
location.content.contentPropertiesType = (_metadata_fb.ContentProperties.BoundingBoxProperties) location.content.range = _metadata_fb.ValueRangeT() location.content.range.min = 2 location.content.range.max = 2 category = _metadata_fb.TensorMetadataT() category.name = "category" category.description = "The categories of the detected boxes. " category.content = _metadata_fb.ContentT() category.content.contentPropertiesType = (_metadata_fb.ContentProperties.FeatureProperties) category.content.contentProperties = (_metadata_fb.FeaturePropertiesT()) category.content.range = _metadata_fb.ValueRangeT() category.content.range.min = 2 category.content.range.max = 2 label_file = _metadata_fb.AssociatedFileT() label_file.name = os.path.basename(labelmap_file) label_file.description = 'Label of objects that this model can recognize. ' label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS category.associatedFiles = [label_file] score = _metadata_fb.TensorMetadataT() score.name = "score" score.description = "The score of the detected boxes. " score.content = _metadata_fb.ContentT() score.content.contentPropertiesType = (_metadata_fb.ContentProperties.FeatureProperties) score.content.contentProperties = (_metadata_fb.FeaturePropertiesT()) score.content.range = _metadata_fb.ValueRangeT() score.content.range.max = 2 score.content.range.min = 2
def create_metadata(model_file_name, label_map_file_name, num_labels):
from tflite_support import flatbuffers
from tflite_support import metadata as _metadata
from tflite_support import metadata_schema_py_generated as _metadata_fb
""" ... """
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = 'MobileNetV1 image classifier'
model_meta.description = ('Identify the most prominent object in the '
'image from a set of 1,001 categories such as '
'trees, animals, food, vehicles, person etc.')
model_meta.version = 'v1'
model_meta.author = 'TensorFlow'
model_meta.license = ('Apache License. Version 2.0 '
'http://www.apache.org/licenses/LICENSE-2.0.')
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = 'image'
input_meta.description = (
'Input image to be classified. The expected image is {0} x {1}, with '
'three channels (red, blue, and green) per pixel. Each value in the '
'tensor is a single byte between 0 and 255.'.format(416, 416))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = [127.5]
input_normalization.options.std = [127.5]
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [255]
input_stats.min = [0]
input_meta.stats = input_stats
# Creates output info.
bbox_meta = _metadata_fb.TensorMetadataT()
bbox_meta.name = 'bbox'
bbox_meta.description = '.'
bbox_meta.content = _metadata_fb.ContentT()
bbox_meta.content.content_properties = _metadata_fb.FeaturePropertiesT()
bbox_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
bbox_stats = _metadata_fb.StatsT()
bbox_stats.max = [416.0]
bbox_stats.min = [0.0]
bbox_meta.stats = bbox_stats
classes_meta = _metadata_fb.TensorMetadataT()
classes_meta.name = 'classes'
classes_meta.description = '.'
classes_meta.content = _metadata_fb.ContentT()
classes_meta.content.content_properties = _metadata_fb.FeaturePropertiesT()
classes_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
classes_stats = _metadata_fb.StatsT()
classes_stats.max = [num_labels]
classes_stats.min = [0]
classes_meta.stats = classes_stats
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(label_map_file_name)
label_file.description = 'Labels for objects that the model can recognize.'
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
classes_meta.associatedFiles = [label_file]
confidence_meta = _metadata_fb.TensorMetadataT()
confidence_meta.name = 'confidence'
confidence_meta.description = '.'
confidence_meta.content = _metadata_fb.ContentT()
confidence_meta.content.content_properties = _metadata_fb.FeaturePropertiesT(
)
confidence_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
confidence_stats = _metadata_fb.StatsT()
confidence_stats.max = [1.0]
confidence_stats.min = [0.0]
confidence_meta.stats = confidence_stats
num_dets_meta = _metadata_fb.TensorMetadataT()
num_dets_meta.name = 'num_dets'
num_dets_meta.description = '.'
num_dets_meta.content = _metadata_fb.ContentT()
num_dets_meta.content.content_properties = _metadata_fb.FeaturePropertiesT(
)
num_dets_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
num_dets_stats = _metadata_fb.StatsT()
num_dets_stats.max = [200]
num_dets_stats.min = [0]
num_dets_meta.stats = num_dets_stats
raw_output_meta = _metadata_fb.TensorMetadataT()
raw_output_meta.name = 'raw_output'
raw_output_meta.description = '.'
raw_output_meta.content = _metadata_fb.ContentT()
raw_output_meta.content.content_properties = _metadata_fb.FeaturePropertiesT(
)
raw_output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
bbox_meta, classes_meta, confidence_meta, num_dets_stats
] # raw_output_meta
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
populator = _metadata.MetadataPopulator.with_model_file(model_file_name)
populator.load_metadata_buffer(metadata_buf)
populator.load_associated_files([label_map_file_name])
populator.populate()
def _create_metadata(self):
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = ("Equipment.")
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"The expected image is 300 x 300, with three channels "
"(re, blue, and green) per pixel. Each value in the tensor is between"
" 0 and 1.")
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates output info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 2
output_location_meta.content.range.max = 2
output_class_meta = _metadata_fb.TensorMetadataT()
output_class_meta.name = "category"
output_class_meta.description = "The categories of the detected boxes."
output_class_meta.content = _metadata_fb.ContentT()
output_class_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_class_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_class_meta.content.range = _metadata_fb.ValueRangeT()
output_class_meta.content.range.min = 2
output_class_meta.content.range.max = 2
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.label_file_path)
label_file.description = "Label of objects that this model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
output_class_meta.associatedFiles = [label_file]
output_score_meta = _metadata_fb.TensorMetadataT()
output_score_meta.name = "score"
output_score_meta.description = "The scores of the detected boxes."
output_score_meta.content = _metadata_fb.ContentT()
output_score_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_score_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_score_meta.content.range = _metadata_fb.ValueRangeT()
output_score_meta.content.range.min = 2
output_score_meta.content.range.max = 2
output_number_meta = _metadata_fb.TensorMetadataT()
output_number_meta.name = "number of detections"
output_number_meta.description = "The number of the detected boxes."
output_number_meta.content = _metadata_fb.ContentT()
output_number_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_number_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
# Creates subgraph info.
group = _metadata_fb.TensorGroupT()
group.name = "detection result"
group.tensorNames = [
output_location_meta.name, output_class_meta.name,
output_score_meta.name
]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
output_location_meta, output_class_meta, output_score_meta,
output_number_meta
]
subgraph.outputTensorGroups = [group]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
def _create_metadata(self):
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = "Letter Recognition Model for handwrite letter recognition"
model_meta.version = self.model_info.version
model_meta.author = "Sebastainj1w"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "sensor_data"
input_meta.description = "Input sensor data to be recognition. The expected data is 252 x 9, with one " \
"channels per pixel. Each value in the tensor is a float32. "
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.GRAYSCALE)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
# input_normalization.options = _metadata_fb.NormalizationOptionsT()
# input_normalization.options.mean = self.model_info.mean
# input_normalization.options.std = self.model_info.std
# input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates output info.
output_meta = _metadata_fb.TensorMetadataT()
output_meta.name = "probability"
output_meta.description = "Probabilities of the %d labels respectively." % self.model_info.num_classes
output_meta.content = _metadata_fb.ContentT()
output_meta.content.content_properties = _metadata_fb.FeaturePropertiesT()
output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_stats = _metadata_fb.StatsT()
output_stats.max = [1.0]
output_stats.min = [0.0]
output_meta.stats = output_stats
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.label_file_path)
label_file.description = "Labels for objects that the model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
output_meta.associatedFiles = [label_file]
# Creates subgraph info.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output_meta]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(
model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
