Adds smaller MobileBERT model.
PiperOrigin-RevId: 501451414
This commit is contained in:
MediaPipe Team
Copybara-Service
parent
8830eefa0b
commit
9cbb76939d
45
mediapipe/model_maker/models/text_classifier/BUILD
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45
mediapipe/model_maker/models/text_classifier/BUILD
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@ -0,0 +1,45 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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load(
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"//mediapipe/framework/tool:mediapipe_files.bzl",
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"mediapipe_files",
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)
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licenses(["notice"])
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package(
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default_visibility = ["//mediapipe/model_maker/python/text/text_classifier:__subpackages__"],
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)
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mediapipe_files(
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srcs = [
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"mobilebert_tiny/assets/vocab.txt",
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"mobilebert_tiny/keras_metadata.pb",
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"mobilebert_tiny/saved_model.pb",
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"mobilebert_tiny/variables/variables.data-00000-of-00001",
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"mobilebert_tiny/variables/variables.index",
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],
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)
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filegroup(
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name = "mobilebert_tiny",
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srcs = [
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"mobilebert_tiny/assets/vocab.txt",
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"mobilebert_tiny/keras_metadata.pb",
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"mobilebert_tiny/saved_model.pb",
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"mobilebert_tiny/variables/variables.data-00000-of-00001",
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"mobilebert_tiny/variables/variables.index",
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],
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)
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@ -53,6 +53,7 @@ py_library(
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deps = [
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":model_options",
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"//mediapipe/model_maker/python/core:hyperparameters",
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"//mediapipe/model_maker/python/core/utils:file_util",
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"//mediapipe/model_maker/python/text/core:bert_model_spec",
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],
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)
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@ -88,6 +89,9 @@ py_library(
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py_test(
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name = "preprocessor_test",
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srcs = ["preprocessor_test.py"],
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data = [
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"//mediapipe/model_maker/models/text_classifier:mobilebert_tiny",
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],
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tags = ["requires-net:external"],
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deps = [
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":dataset",
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@ -109,6 +113,9 @@ py_library(
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py_library(
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name = "text_classifier",
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srcs = ["text_classifier.py"],
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data = [
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"//mediapipe/model_maker/models/text_classifier:mobilebert_tiny",
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],
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deps = [
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":dataset",
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":model_options",
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@ -130,6 +137,7 @@ py_test(
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size = "large",
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srcs = ["text_classifier_test.py"],
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data = [
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"//mediapipe/model_maker/models/text_classifier:mobilebert_tiny",
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"//mediapipe/model_maker/python/text/text_classifier/testdata",
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],
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tags = ["requires-net:external"],
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@ -151,6 +159,9 @@ py_library(
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py_binary(
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name = "text_classifier_demo",
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srcs = ["text_classifier_demo.py"],
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data = [
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"//mediapipe/model_maker/models/text_classifier:mobilebert_tiny",
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],
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deps = [
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":text_classifier_demo_lib",
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],
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@ -18,12 +18,15 @@ import enum
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import functools
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from mediapipe.model_maker.python.core import hyperparameters as hp
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from mediapipe.model_maker.python.core.utils import file_util
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from mediapipe.model_maker.python.text.core import bert_model_spec
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from mediapipe.model_maker.python.text.text_classifier import model_options as mo
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# BERT-based text classifier spec inherited from BertModelSpec
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BertClassifierSpec = bert_model_spec.BertModelSpec
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MOBILEBERT_TINY_PATH = 'mediapipe/model_maker/models/text_classifier/mobilebert_tiny/'
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@dataclasses.dataclass
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class AverageWordEmbeddingClassifierSpec:
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@ -49,16 +52,14 @@ average_word_embedding_classifier_spec = functools.partial(
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mobilebert_classifier_spec = functools.partial(
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BertClassifierSpec,
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hparams=hp.BaseHParams(
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epochs=3,
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batch_size=48,
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learning_rate=3e-5,
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distribution_strategy='off'),
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epochs=3, batch_size=48, learning_rate=3e-5, distribution_strategy='off'
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),
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name='MobileBert',
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uri='https://tfhub.dev/tensorflow/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1',
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uri=file_util.get_absolute_path(MOBILEBERT_TINY_PATH),
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tflite_input_name={
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'ids': 'serving_default_input_1:0',
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'mask': 'serving_default_input_3:0',
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'segment_ids': 'serving_default_input_2:0'
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'segment_ids': 'serving_default_input_2:0',
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},
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)
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@ -28,9 +28,10 @@ class ModelSpecTest(tf.test.TestCase):
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model_spec_obj = ms.SupportedModels.MOBILEBERT_CLASSIFIER.value()
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self.assertIsInstance(model_spec_obj, ms.BertClassifierSpec)
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self.assertEqual(model_spec_obj.name, 'MobileBert')
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self.assertEqual(
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model_spec_obj.uri, 'https://tfhub.dev/tensorflow/'
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'mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1')
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self.assertIn(
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'mediapipe/model_maker/models/text_classifier/mobilebert_tiny',
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model_spec_obj.uri,
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)
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self.assertTrue(model_spec_obj.do_lower_case)
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self.assertEqual(
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model_spec_obj.tflite_input_name, {
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@ -19,5 +19,8 @@ package(
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filegroup(
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name = "testdata",
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srcs = ["average_word_embedding_metadata.json"],
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srcs = [
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"average_word_embedding_metadata.json",
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"bert_metadata.json",
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],
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)
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84
mediapipe/model_maker/python/text/text_classifier/testdata/bert_metadata.json
vendored
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84
mediapipe/model_maker/python/text/text_classifier/testdata/bert_metadata.json
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@ -0,0 +1,84 @@
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{
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"name": "TextClassifier",
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"description": "Classify the input text into a set of known categories.",
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"subgraph_metadata": [
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{
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"input_tensor_metadata": [
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{
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"name": "ids",
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"description": "Tokenized ids of the input text.",
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"content": {
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"content_properties_type": "FeatureProperties",
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"content_properties": {
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}
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},
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"stats": {
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}
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},
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{
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"name": "mask",
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"description": "Mask with 1 for real tokens and 0 for padding tokens.",
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"content": {
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"content_properties_type": "FeatureProperties",
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"content_properties": {
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}
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},
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"stats": {
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}
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},
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{
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"name": "segment_ids",
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"description": "0 for the first sequence, 1 for the second sequence if exists.",
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"content": {
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"content_properties_type": "FeatureProperties",
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"content_properties": {
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}
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},
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"stats": {
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}
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}
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],
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"output_tensor_metadata": [
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{
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"name": "score",
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"description": "Score of the labels respectively.",
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"content": {
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"content_properties_type": "FeatureProperties",
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"content_properties": {
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}
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},
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"stats": {
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"max": [
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1.0
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],
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"min": [
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0.0
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]
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},
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"associated_files": [
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{
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"name": "labels.txt",
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"description": "Labels for categories that the model can recognize.",
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"type": "TENSOR_AXIS_LABELS"
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}
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]
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}
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],
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"input_process_units": [
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{
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"options_type": "BertTokenizerOptions",
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"options": {
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"vocab_file": [
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{
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"name": "vocab.txt",
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"description": "Vocabulary file to convert natural language words to embedding vectors.",
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"type": "VOCABULARY"
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}
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]
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}
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}
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]
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}
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],
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"min_parser_version": "1.1.0"
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}
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@ -269,16 +269,21 @@ class _AverageWordEmbeddingClassifier(TextClassifier):
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"""Creates an Average Word Embedding model."""
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self._model = tf.keras.Sequential([
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tf.keras.layers.InputLayer(
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input_shape=[self._model_options.seq_len], dtype=tf.int32),
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input_shape=[self._model_options.seq_len],
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dtype=tf.int32,
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name="input_ids",
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),
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tf.keras.layers.Embedding(
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len(self._text_preprocessor.get_vocab()),
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self._model_options.wordvec_dim,
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input_length=self._model_options.seq_len),
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input_length=self._model_options.seq_len,
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),
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tf.keras.layers.GlobalAveragePooling1D(),
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tf.keras.layers.Dense(
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self._model_options.wordvec_dim, activation=tf.nn.relu),
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self._model_options.wordvec_dim, activation=tf.nn.relu
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),
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tf.keras.layers.Dropout(self._model_options.dropout_rate),
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tf.keras.layers.Dense(self._num_classes, activation="softmax")
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tf.keras.layers.Dense(self._num_classes, activation="softmax"),
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])
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def _save_vocab(self, vocab_filepath: str):
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@ -26,6 +26,9 @@ class TextClassifierTest(tf.test.TestCase):
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_AVERAGE_WORD_EMBEDDING_JSON_FILE = (
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test_utils.get_test_data_path('average_word_embedding_metadata.json'))
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_BERT_CLASSIFIER_JSON_FILE = test_utils.get_test_data_path(
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'bert_metadata.json'
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)
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def _get_data(self):
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labels_and_text = (('pos', 'super good'), (('neg', 'really bad')))
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@ -94,7 +97,27 @@ class TextClassifierTest(tf.test.TestCase):
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_, accuracy = bert_classifier.evaluate(validation_data)
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self.assertGreaterEqual(accuracy, 0.0)
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# TODO: Add a unit test that does not run OOM.
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# Test export_model
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bert_classifier.export_model()
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output_metadata_file = os.path.join(
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options.hparams.export_dir, 'metadata.json'
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)
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output_tflite_file = os.path.join(
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options.hparams.export_dir, 'model.tflite'
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)
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self.assertTrue(os.path.exists(output_tflite_file))
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self.assertGreater(os.path.getsize(output_tflite_file), 0)
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self.assertTrue(os.path.exists(output_metadata_file))
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self.assertGreater(os.path.getsize(output_metadata_file), 0)
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filecmp.clear_cache()
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self.assertTrue(
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filecmp.cmp(
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output_metadata_file, self._BERT_CLASSIFIER_JSON_FILE, shallow=False
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)
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)
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def test_label_mismatch(self):
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options = (
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@ -81,7 +81,10 @@ def _setup_build_dir():
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file.write(filedata)
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# Use bazel to download GCS model files
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model_build_files = ['models/gesture_recognizer/BUILD']
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model_build_files = [
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'models/gesture_recognizer/BUILD',
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'models/text_classifier/BUILD',
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]
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for model_build_file in model_build_files:
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build_target_file = os.path.join(BUILD_MM_DIR, model_build_file)
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os.makedirs(os.path.dirname(build_target_file), exist_ok=True)
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@ -95,7 +98,12 @@ def _setup_build_dir():
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'models/gesture_recognizer/gesture_embedder/saved_model.pb',
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'models/gesture_recognizer/gesture_embedder/variables/variables.data-00000-of-00001',
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'models/gesture_recognizer/gesture_embedder/variables/variables.index',
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]
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'models/text_classifier/mobilebert_tiny/keras_metadata.pb',
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'models/text_classifier/mobilebert_tiny/saved_model.pb',
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'models/text_classifier/mobilebert_tiny/assets/vocab.txt',
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'models/text_classifier/mobilebert_tiny/variables/variables.data-00000-of-00001',
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'models/text_classifier/mobilebert_tiny/variables/variables.index',
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]
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for elem in external_files:
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external_file = os.path.join(f'{SRC_NAME}/mediapipe_model_maker', elem)
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sys.stderr.write('downloading file: %s\n' % external_file)
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30
third_party/external_files.bzl
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30
third_party/external_files.bzl
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@ -1006,6 +1006,18 @@ def external_files():
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urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/saved_model.pb?generation=1668550484904822"],
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)
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http_file(
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name = "com_google_mediapipe_mobilebert_tiny_keras_metadata_pb",
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sha256 = "cef8131a414c602b9d4742ac57f4f90bc5d8a42baec36b65deece884e2d0cf0f",
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urls = ["https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny/keras_metadata.pb?generation=1673297965144159"],
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)
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http_file(
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name = "com_google_mediapipe_mobilebert_tiny_saved_model_pb",
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sha256 = "323c997cd3e17df1b2e3bdebe3cfe2b17c5ffd9488a26a4afb59ee819196837a",
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urls = ["https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny/saved_model.pb?generation=1673297968138825"],
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)
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http_file(
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name = "com_google_mediapipe_object_detection_saved_model_model_ckpt_data-00000-of-00001",
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sha256 = "ad2f733f271dd5000a8c7f926bfea1083e6408b34d4f3b60679e5a6f96251c97",
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@ -1053,3 +1065,21 @@ def external_files():
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sha256 = "76ea482b8da6bdb3d65d3b2ea989c1699c9fa0d6df0cb6d80863d1dc6fe7c4bd",
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urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/variables/variables.index?generation=1668550490691823"],
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)
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http_file(
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name = "com_google_mediapipe_mobilebert_tiny_assets_vocab_txt",
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sha256 = "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3",
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urls = ["https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny/assets/vocab.txt?generation=1673297970948751"],
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)
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http_file(
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name = "com_google_mediapipe_mobilebert_tiny_variables_variables_data-00000-of-00001",
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sha256 = "c3857370046cd3a2f345657cf1bb259a4e7e09185d7f0808e57803e9d41ebba4",
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urls = ["https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny/variables/variables.data-00000-of-00001?generation=1673297975132568"],
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)
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http_file(
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name = "com_google_mediapipe_mobilebert_tiny_variables_variables_index",
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sha256 = "4df4d7c0fefe99903ab6ebf44b7478196ce613082d2ca692a5a37a7f24e562ed",
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urls = ["https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny/variables/variables.index?generation=1673297977586840"],
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)
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