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Metadata Writer: Add Metadata Writer for image classifier.

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PiperOrigin-RevId: 483282627
This commit is contained in:
Yuqi Li 2022-10-23 23:03:44 -07:00 committed by Copybara-Service
parent ec2a34d2a4
commit ab17be9294
11 changed files with 1537 additions and 28 deletions
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6
mediapipe/tasks/python/metadata/metadata_writers/BUILD
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@ -37,3 +37,9 @@ py_library(
srcs = ["writer_utils.py"],
deps = ["//mediapipe/tasks/metadata:schema_py"],
)
py_library(
name = "image_classifier",
srcs = ["image_classifier.py"],
deps = [":metadata_writer"],
)

71
mediapipe/tasks/python/metadata/metadata_writers/image_classifier.py Normal file
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@ -0,0 +1,71 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Writes metadata and label file to the image classifier models."""
from typing import List, Optional
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
_MODEL_NAME = "ImageClassifier"
_MODEL_DESCRIPTION = ("Identify the most prominent object in the image from a "
"known set of categories.")
class MetadataWriter(metadata_writer.MetadataWriterBase):
"""MetadataWriter to write the metadata for image classifier."""
@classmethod
def create(
cls,
model_buffer: bytearray,
input_norm_mean: List[float],
input_norm_std: List[float],
labels: metadata_writer.Labels,
score_calibration: Optional[metadata_writer.ScoreCalibration] = None
) -> "MetadataWriter":
"""Creates MetadataWriter to write the metadata for image classifier.
The parameters required in this method are mandatory when using MediaPipe
Tasks.
Note that only the output TFLite is used for deployment. The output JSON
content is used to interpret the metadata content.
Args:
model_buffer: A valid flatbuffer loaded from the TFLite model file.
input_norm_mean: the mean value used in the input tensor normalization
[1].
input_norm_std: the std value used in the input tensor normalizarion [1].
labels: an instance of Labels helper class used in the output
classification tensor [2].
score_calibration: A container of the score calibration operation [3] in
the classification tensor. Optional if the model does not use score
calibration.
[1]:
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
[2]:
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
[3]:
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
Returns:
An MetadataWrite object.
"""
writer = metadata_writer.MetadataWriter(model_buffer)
writer.add_genernal_info(_MODEL_NAME, _MODEL_DESCRIPTION)
writer.add_image_input(input_norm_mean, input_norm_std)
writer.add_classification_output(labels, score_calibration)
return cls(writer)

128
mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py
View File

@ -15,19 +15,22 @@
"""Generic metadata writer."""
import collections
import csv
import dataclasses
import os
import tempfile
from typing import List, Optional, Tuple
import flatbuffers
from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
from mediapipe.tasks.python.metadata import metadata as _metadata
from mediapipe.tasks.metadata import metadata_schema_py_generated as metadata_fb
from mediapipe.tasks.python.metadata import metadata
from mediapipe.tasks.python.metadata.metadata_writers import metadata_info
from mediapipe.tasks.python.metadata.metadata_writers import writer_utils
_INPUT_IMAGE_NAME = 'image'
_INPUT_IMAGE_DESCRIPTION = 'Input image to be processed.'
_OUTPUT_CLASSIFICATION_NAME = 'score'
_OUTPUT_CLASSIFICATION_DESCRIPTION = 'Score of the labels respectively.'
@dataclasses.dataclass
@ -140,26 +143,85 @@ class Labels(object):
class ScoreCalibration:
"""Simple container holding score calibration related parameters."""
# A shortcut to avoid client side code importing _metadata_fb
transformation_types = _metadata_fb.ScoreTransformationType
# A shortcut to avoid client side code importing metadata_fb
transformation_types = metadata_fb.ScoreTransformationType
def __init__(self,
transformation_type: _metadata_fb.ScoreTransformationType,
parameters: List[CalibrationParameter],
transformation_type: metadata_fb.ScoreTransformationType,
parameters: List[Optional[CalibrationParameter]],
default_score: int = 0):
self.transformation_type = transformation_type
self.parameters = parameters
self.default_score = default_score
@classmethod
def create_from_file(cls,
transformation_type: metadata_fb.ScoreTransformationType,
file_path: str,
default_score: int = 0) -> 'ScoreCalibration':
"""Creates ScoreCalibration from the file.
Args:
transformation_type: type of the function used for transforming the
uncalibrated score before applying score calibration.
file_path: file_path of the score calibration file [1]. Contains
sigmoid-based score calibration parameters, formatted as CSV. Lines
contain for each index of an output tensor the scale, slope, offset and
(optional) min_score parameters to be used for sigmoid fitting (in this
order and in `strtof`-compatible [2] format). Scale should be a
non-negative value. A line may be left empty to default calibrated
scores for this index to default_score. In summary, each line should
thus contain 0, 3 or 4 comma-separated values.
default_score: the default calibrated score to apply if the uncalibrated
score is below min_score or if no parameters were specified for a given
index.
[1]:
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L133
[2]:
https://en.cppreference.com/w/c/string/byte/strtof
Returns:
A ScoreCalibration object.
Raises:
ValueError: if the score_calibration file is malformed.
"""
with open(file_path, 'r') as calibration_file:
csv_reader = csv.reader(calibration_file, delimiter=',')
parameters = []
for row in csv_reader:
if not row:
parameters.append(None)
continue
if len(row) != 3 and len(row) != 4:
raise ValueError(
f'Expected empty lines or 3 or 4 parameters per line in score'
f' calibration file, but got {len(row)}.')
if float(row[0]) < 0:
raise ValueError(
f'Expected scale to be a non-negative value, but got '
f'{float(row[0])}.')
parameters.append(
CalibrationParameter(
scale=float(row[0]),
slope=float(row[1]),
offset=float(row[2]),
min_score=None if len(row) == 3 else float(row[3])))
return cls(transformation_type, parameters, default_score)
def _fill_default_tensor_names(
tensor_metadata: List[_metadata_fb.TensorMetadataT],
tensor_metadata_list: List[metadata_fb.TensorMetadataT],
tensor_names_from_model: List[str]):
"""Fills the default tensor names."""
# If tensor name in metadata is empty, default to the tensor name saved in
# the model.
for metadata, name in zip(tensor_metadata, tensor_names_from_model):
metadata.name = metadata.name or name
for tensor_metadata, name in zip(tensor_metadata_list,
tensor_names_from_model):
tensor_metadata.name = tensor_metadata.name or name
def _pair_tensor_metadata(
@ -212,7 +274,7 @@ def _create_metadata_buffer(
input_metadata = [m.create_metadata() for m in input_md]
else:
num_input_tensors = writer_utils.get_subgraph(model_buffer).InputsLength()
input_metadata = [_metadata_fb.TensorMetadataT()] * num_input_tensors
input_metadata = [metadata_fb.TensorMetadataT()] * num_input_tensors
_fill_default_tensor_names(input_metadata,
writer_utils.get_input_tensor_names(model_buffer))
@ -224,12 +286,12 @@ def _create_metadata_buffer(
output_metadata = [m.create_metadata() for m in output_md]
else:
num_output_tensors = writer_utils.get_subgraph(model_buffer).OutputsLength()
output_metadata = [_metadata_fb.TensorMetadataT()] * num_output_tensors
output_metadata = [metadata_fb.TensorMetadataT()] * num_output_tensors
_fill_default_tensor_names(output_metadata,
writer_utils.get_output_tensor_names(model_buffer))
# Create the subgraph metadata.
subgraph_metadata = _metadata_fb.SubGraphMetadataT()
subgraph_metadata = metadata_fb.SubGraphMetadataT()
subgraph_metadata.inputTensorMetadata = input_metadata
subgraph_metadata.outputTensorMetadata = output_metadata
@ -243,7 +305,7 @@ def _create_metadata_buffer(
b = flatbuffers.Builder(0)
b.Finish(
model_metadata.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
return b.Output()
@ -291,7 +353,7 @@ class MetadataWriter(object):
name=model_name, description=model_description)
return self
color_space_types = _metadata_fb.ColorSpaceType
color_space_types = metadata_fb.ColorSpaceType
def add_feature_input(self,
name: Optional[str] = None,
@ -305,7 +367,7 @@ class MetadataWriter(object):
self,
norm_mean: List[float],
norm_std: List[float],
color_space_type: Optional[int] = _metadata_fb.ColorSpaceType.RGB,
color_space_type: Optional[int] = metadata_fb.ColorSpaceType.RGB,
name: str = _INPUT_IMAGE_NAME,
description: str = _INPUT_IMAGE_DESCRIPTION) -> 'MetadataWriter':
"""Adds an input image metadata for the image input.
@ -341,9 +403,6 @@ class MetadataWriter(object):
self._input_mds.append(input_md)
return self
_OUTPUT_CLASSIFICATION_NAME = 'score'
_OUTPUT_CLASSIFICATION_DESCRIPTION = 'Score of the labels respectively'
def add_classification_output(
self,
labels: Optional[Labels] = None,
@ -416,8 +475,7 @@ class MetadataWriter(object):
A tuple of (model_with_metadata_in_bytes, metdata_json_content)
"""
# Populates metadata and associated files into TFLite model buffer.
populator = _metadata.MetadataPopulator.with_model_buffer(
self._model_buffer)
populator = metadata.MetadataPopulator.with_model_buffer(self._model_buffer)
metadata_buffer = _create_metadata_buffer(
model_buffer=self._model_buffer,
general_md=self._general_md,
@ -429,7 +487,7 @@ class MetadataWriter(object):
populator.populate()
tflite_content = populator.get_model_buffer()
displayer = _metadata.MetadataDisplayer.with_model_buffer(tflite_content)
displayer = metadata.MetadataDisplayer.with_model_buffer(tflite_content)
metadata_json_content = displayer.get_metadata_json()
return tflite_content, metadata_json_content
@ -452,9 +510,7 @@ class MetadataWriter(object):
"""Stores calibration parameters in a csv file."""
filepath = os.path.join(self._temp_folder.name, filename)
with open(filepath, 'w') as f:
for idx, item in enumerate(calibrations):
if idx != 0:
f.write('\n')
for item in calibrations:
if item:
if item.scale is None or item.slope is None or item.offset is None:
raise ValueError('scale, slope and offset values can not be set to '
@ -463,6 +519,30 @@ class MetadataWriter(object):
f.write(f'{item.scale},{item.slope},{item.offset},{item.min_score}')
else:
f.write(f'{item.scale},{item.slope},{item.offset}')
f.write('\n')
self._associated_files.append(filepath)
return filepath
class MetadataWriterBase:
"""Base MetadataWriter class which contains the apis exposed to users.
MetadataWriter for Tasks e.g. image classifier / object detector will inherit
this class for their own usage.
"""
def __init__(self, writer: MetadataWriter) -> None:
self.writer = writer
def populate(self) -> Tuple[bytearray, str]:
"""Populates metadata into the TFLite file.
Note that only the output tflite is used for deployment. The output JSON
content is used to interpret the metadata content.
Returns:
A tuple of (model_with_metadata_in_bytes, metdata_json_content)
"""
return self.writer.populate()

21
mediapipe/tasks/python/test/metadata/metadata_writers/BUILD
View File

@ -28,9 +28,28 @@ py_test(
py_test(
name = "metadata_writer_test",
srcs = ["metadata_writer_test.py"],
data = ["//mediapipe/tasks/testdata/metadata:model_files"],
data = [
"//mediapipe/tasks/testdata/metadata:data_files",
"//mediapipe/tasks/testdata/metadata:model_files",
],
deps = [
"//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
"//mediapipe/tasks/python/test:test_utils",
],
)
py_test(
name = "image_classifier_test",
srcs = ["image_classifier_test.py"],
data = [
"//mediapipe/tasks/testdata/metadata:data_files",
"//mediapipe/tasks/testdata/metadata:model_files",
],
deps = [
"//mediapipe/tasks/metadata:metadata_schema_py",
"//mediapipe/tasks/python/metadata",
"//mediapipe/tasks/python/metadata/metadata_writers:image_classifier",
"//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
"//mediapipe/tasks/python/test:test_utils",
],
)

76
mediapipe/tasks/python/test/metadata/metadata_writers/image_classifier_test.py Normal file
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@ -0,0 +1,76 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for metadata_writer.image_classifier."""
from absl.testing import absltest
from absl.testing import parameterized
from mediapipe.tasks.metadata import metadata_schema_py_generated as metadata_fb
from mediapipe.tasks.python.metadata import metadata
from mediapipe.tasks.python.metadata.metadata_writers import image_classifier
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
from mediapipe.tasks.python.test import test_utils
_FLOAT_MODEL = test_utils.get_test_data_path(
"mobilenet_v2_1.0_224_without_metadata.tflite")
_QUANT_MODEL = test_utils.get_test_data_path(
"mobilenet_v2_1.0_224_quant_without_metadata.tflite")
_LABEL_FILE = test_utils.get_test_data_path("labels.txt")
_SCORE_CALIBRATION_FILE = test_utils.get_test_data_path("score_calibration.txt")
_SCORE_CALIBRATION_FILENAME = "score_calibration.txt"
_DEFAULT_SCORE_CALIBRATION_VALUE = 0.2
_NORM_MEAN = 127.5
_NORM_STD = 127.5
_FLOAT_JSON = test_utils.get_test_data_path("mobilenet_v2_1.0_224.json")
_QUANT_JSON = test_utils.get_test_data_path("mobilenet_v2_1.0_224_quant.json")
class ImageClassifierTest(parameterized.TestCase):
@parameterized.named_parameters(
{
"testcase_name": "test_float_model",
"model_file": _FLOAT_MODEL,
"golden_json": _FLOAT_JSON
}, {
"testcase_name": "test_quant_model",
"model_file": _QUANT_MODEL,
"golden_json": _QUANT_JSON
})
def test_write_metadata(self, model_file: str, golden_json: str):
with open(model_file, "rb") as f:
model_buffer = f.read()
writer = image_classifier.MetadataWriter.create(
model_buffer, [_NORM_MEAN], [_NORM_STD],
labels=metadata_writer.Labels().add_from_file(_LABEL_FILE),
score_calibration=metadata_writer.ScoreCalibration.create_from_file(
metadata_fb.ScoreTransformationType.LOG, _SCORE_CALIBRATION_FILE,
_DEFAULT_SCORE_CALIBRATION_VALUE))
tflite_content, metadata_json = writer.populate()
with open(golden_json, "r") as f:
expected_json = f.read()
self.assertEqual(metadata_json, expected_json)
displayer = metadata.MetadataDisplayer.with_model_buffer(tflite_content)
file_buffer = displayer.get_associated_file_buffer(
_SCORE_CALIBRATION_FILENAME)
with open(_SCORE_CALIBRATION_FILE, "rb") as f:
expected_file_buffer = f.read()
self.assertEqual(file_buffer, expected_file_buffer)
if __name__ == "__main__":
absltest.main()

56
mediapipe/tasks/python/test/metadata/metadata_writers/metadata_writer_test.py
View File

@ -13,6 +13,9 @@
# limitations under the License.
# ==============================================================================
"""Tests for metadata writer classes."""
import os
import tempfile
from absl.testing import absltest
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
@ -20,6 +23,7 @@ from mediapipe.tasks.python.test import test_utils
_IMAGE_CLASSIFIER_MODEL = test_utils.get_test_data_path(
'mobilenet_v1_0.25_224_1_default_1.tflite')
_SCORE_CALIBRATION_FILE = test_utils.get_test_data_path('score_calibration.txt')
class LabelsTest(absltest.TestCase):
@ -49,6 +53,54 @@ class LabelsTest(absltest.TestCase):
])
class ScoreCalibrationTest(absltest.TestCase):
def test_create_from_file_successful(self):
score_calibration = metadata_writer.ScoreCalibration.create_from_file(
metadata_writer.ScoreCalibration.transformation_types.LOG,
_SCORE_CALIBRATION_FILE)
self.assertLen(score_calibration.parameters, 511)
self.assertIsNone(score_calibration.parameters[0])
self.assertEqual(
score_calibration.parameters[1],
metadata_writer.CalibrationParameter(
scale=0.9876328110694885,
slope=0.36622241139411926,
offset=0.5352765321731567,
min_score=0.71484375))
self.assertEqual(
score_calibration.parameters[510],
metadata_writer.CalibrationParameter(
scale=0.9901729226112366,
slope=0.8561913371086121,
offset=0.8783953189849854,
min_score=0.5859375))
def test_create_from_file_fail(self):
with tempfile.TemporaryDirectory() as temp_dir:
test_file = os.path.join(temp_dir, 'score_calibration.csv')
with open(test_file, 'w') as f:
f.write('0.98,0.5\n')
with self.assertRaisesRegex(
ValueError,
'Expected empty lines or 3 or 4 parameters per line in score '
'calibration file, but got 2.'
):
metadata_writer.ScoreCalibration.create_from_file(
metadata_writer.ScoreCalibration.transformation_types.LOG,
test_file)
with open(test_file, 'w') as f:
f.write('-0.98,0.5,0.34\n')
with self.assertRaisesRegex(
ValueError,
'Expected scale to be a non-negative value, but got -0.98.'):
metadata_writer.ScoreCalibration.create_from_file(
metadata_writer.ScoreCalibration.transformation_types.LOG,
test_file)
class MetadataWriterForTaskTest(absltest.TestCase):
def setUp(self):
@ -197,7 +249,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
"output_tensor_metadata": [
{
"name": "score",
"description": "Score of the labels respectively",
"description": "Score of the labels respectively.",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {
@ -298,7 +350,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
"output_tensor_metadata": [
{
"name": "score",
"description": "Score of the labels respectively",
"description": "Score of the labels respectively.",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {

10
mediapipe/tasks/testdata/metadata/BUILD vendored
View File

@ -29,6 +29,8 @@ mediapipe_files(srcs = [
"mobile_object_classifier_v0_2_3-metadata-no-name.tflite",
"mobilenet_v1_0.25_224_1_default_1.tflite",
"mobilenet_v2_1.0_224_quant.tflite",
"mobilenet_v2_1.0_224_quant_without_metadata.tflite",
"mobilenet_v2_1.0_224_without_metadata.tflite",
])
exports_files([
@ -48,6 +50,9 @@ exports_files([
"score_calibration.txt",
"score_calibration_file_meta.json",
"score_calibration_tensor_meta.json",
"labels.txt",
"mobilenet_v2_1.0_224.json",
"mobilenet_v2_1.0_224_quant.json",
])
filegroup(
@ -59,6 +64,8 @@ filegroup(
"mobile_object_classifier_v0_2_3-metadata-no-name.tflite",
"mobilenet_v1_0.25_224_1_default_1.tflite",
"mobilenet_v2_1.0_224_quant.tflite",
"mobilenet_v2_1.0_224_quant_without_metadata.tflite",
"mobilenet_v2_1.0_224_without_metadata.tflite",
],
)
@ -78,6 +85,9 @@ filegroup(
"input_image_tensor_float_meta.json",
"input_image_tensor_uint8_meta.json",
"input_image_tensor_unsupported_meta.json",
"labels.txt",
"mobilenet_v2_1.0_224.json",
"mobilenet_v2_1.0_224_quant.json",
"score_calibration.txt",
"score_calibration_file_meta.json",
"score_calibration_tensor_meta.json",

1001
mediapipe/tasks/testdata/metadata/labels.txt vendored Normal file
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File diff suppressed because it is too large Load Diff

82
mediapipe/tasks/testdata/metadata/mobilenet_v2_1.0_224.json vendored Normal file
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@ -0,0 +1,82 @@
{
"name": "ImageClassifier",
"description": "Identify the most prominent object in the image from a known set of categories.",
"subgraph_metadata": [
{
"input_tensor_metadata": [
{
"name": "image",
"description": "Input image to be processed.",
"content": {
"content_properties_type": "ImageProperties",
"content_properties": {
"color_space": "RGB"
}
},
"process_units": [
{
"options_type": "NormalizationOptions",
"options": {
"mean": [
127.5
],
"std": [
127.5
]
}
}
],
"stats": {
"max": [
1.0
],
"min": [
-1.0
]
}
}
],
"output_tensor_metadata": [
{
"name": "score",
"description": "Score of the labels respectively.",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {
}
},
"process_units": [
{
"options_type": "ScoreCalibrationOptions",
"options": {
"score_transformation": "LOG",
"default_score": 0.2
}
}
],
"stats": {
"max": [
1.0
],
"min": [
0.0
]
},
"associated_files": [
{
"name": "labels.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "score_calibration.txt",
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
}
]
}
]
}
],
"min_parser_version": "1.0.0"
}

82
mediapipe/tasks/testdata/metadata/mobilenet_v2_1.0_224_quant.json vendored Normal file
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@ -0,0 +1,82 @@
{
"name": "ImageClassifier",
"description": "Identify the most prominent object in the image from a known set of categories.",
"subgraph_metadata": [
{
"input_tensor_metadata": [
{
"name": "image",
"description": "Input image to be processed.",
"content": {
"content_properties_type": "ImageProperties",
"content_properties": {
"color_space": "RGB"
}
},
"process_units": [
{
"options_type": "NormalizationOptions",
"options": {
"mean": [
127.5
],
"std": [
127.5
]
}
}
],
"stats": {
"max": [
255.0
],
"min": [
0.0
]
}
}
],
"output_tensor_metadata": [
{
"name": "score",
"description": "Score of the labels respectively.",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {
}
},
"process_units": [
{
"options_type": "ScoreCalibrationOptions",
"options": {
"score_transformation": "LOG",
"default_score": 0.2
}
}
],
"stats": {
"max": [
255.0
],
"min": [
0.0
]
},
"associated_files": [
{
"name": "labels.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "score_calibration.txt",
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
}
]
}
]
}
],
"min_parser_version": "1.0.0"
}

30
third_party/external_files.bzl vendored
View File

@ -364,6 +364,12 @@ def external_files():
urls = ["https://storage.googleapis.com/mediapipe-assets/knift_labelmap.txt?generation=1661875792821628"],
)
http_file(
name = "com_google_mediapipe_labels_txt",
sha256 = "536feacc519de3d418de26b2effb4d75694a8c4c0063e36499a46fa8061e2da9",
urls = ["https://storage.googleapis.com/mediapipe-assets/labels.txt?generation=1665988394538324"],
)
http_file(
name = "com_google_mediapipe_left_hands_jpg",
sha256 = "4b5134daa4cb60465535239535f9f74c2842aba3aa5fd30bf04ef5678f93d87f",
@ -448,18 +454,42 @@ def external_files():
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v1_0.25_224_quant_without_subgraph_metadata.tflite?generation=1661875836078124"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_json",
sha256 = "0eb285a857b4bb1815736d0902ace0af45ea62e90c1dac98844b9ca797cd0d7b",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224.json?generation=1665988398778178"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_quant_json",
sha256 = "932f345ebe3d98daf0dc4c88b0f9e694e450390fb394fc217e851338dfec43e6",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224_quant.json?generation=1665988401522527"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_quant_tflite",
sha256 = "f08d447cde49b4e0446428aa921aff0a14ea589fa9c5817b31f83128e9a43c1d",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224_quant.tflite?generation=1664340173966530"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_quant_without_metadata_tflite",
sha256 = "f08d447cde49b4e0446428aa921aff0a14ea589fa9c5817b31f83128e9a43c1d",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224_quant_without_metadata.tflite?generation=1665988405130772"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_tflite",
sha256 = "ff5cb7f9e62c92ebdad971f8a98aa6b3106d82a64587a7787c6a385c9e791339",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224.tflite?generation=1661875840611150"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v2_1_0_224_without_metadata_tflite",
sha256 = "9f3bc29e38e90842a852bfed957dbf5e36f2d97a91dd17736b1e5c0aca8d3303",
urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenet_v2_1.0_224_without_metadata.tflite?generation=1665988408360823"],
)
http_file(
name = "com_google_mediapipe_mobilenet_v3_small_100_224_embedder_tflite",
sha256 = "f7b9a563cb803bdcba76e8c7e82abde06f5c7a8e67b5e54e43e23095dfe79a78",