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f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | # Copyright 2023 The TensorFlow 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.
"""Classifcation Task Showcase."""
import dataclasses
from typing import List, Mapping, Text
from seqeval import metrics as seqeval_metrics
import tensorflow as tf, tf_keras
from official.core import base_task
from official.core import config_definitions as cfg
from official.core import exp_factory
from official.modeling import optimization
from official.modeling import tf_utils
from official.modeling.hyperparams import base_config
from official.nlp.configs import encoders
from official.nlp.modeling import models
from official.nlp.tasks import utils
from official.projects.text_classification_example import classification_data_loader
@dataclasses.dataclass
class ModelConfig(base_config.Config):
"""A base span labeler configuration."""
encoder: encoders.EncoderConfig = dataclasses.field(
default_factory=encoders.EncoderConfig
)
head_dropout: float = 0.1
head_initializer_range: float = 0.02
@dataclasses.dataclass
class ClassificationExampleConfig(cfg.TaskConfig):
"""The model config."""
# At most one of `init_checkpoint` and `hub_module_url` can be specified.
init_checkpoint: str = ''
hub_module_url: str = ''
model: ModelConfig = dataclasses.field(default_factory=ModelConfig)
num_classes = 2
class_names = ['A', 'B']
train_data: cfg.DataConfig = dataclasses.field(
default_factory=classification_data_loader.ClassificationExampleDataConfig
)
validation_data: cfg.DataConfig = dataclasses.field(
default_factory=classification_data_loader.ClassificationExampleDataConfig
)
class ClassificationExampleTask(base_task.Task):
"""Task object for classification."""
def build_model(self) -> tf_keras.Model:
if self.task_config.hub_module_url and self.task_config.init_checkpoint:
raise ValueError('At most one of `hub_module_url` and '
'`init_checkpoint` can be specified.')
if self.task_config.hub_module_url:
encoder_network = utils.get_encoder_from_hub(
self.task_config.hub_module_url)
else:
encoder_network = encoders.build_encoder(self.task_config.model.encoder)
return models.BertClassifier(
network=encoder_network,
num_classes=len(self.task_config.class_names),
initializer=tf_keras.initializers.TruncatedNormal(
stddev=self.task_config.model.head_initializer_range),
dropout_rate=self.task_config.model.head_dropout)
def build_losses(self, labels, model_outputs, aux_losses=None) -> tf.Tensor:
loss = tf_keras.losses.sparse_categorical_crossentropy(
labels, tf.cast(model_outputs, tf.float32), from_logits=True)
return tf_utils.safe_mean(loss)
def build_inputs(self,
params: cfg.DataConfig,
input_context=None) -> tf.data.Dataset:
"""Returns tf.data.Dataset for sentence_prediction task."""
loader = classification_data_loader.ClassificationDataLoader(params)
return loader.load(input_context)
def inference_step(self, inputs,
model: tf_keras.Model) -> Mapping[str, tf.Tensor]:
"""Performs the forward step."""
logits = model(inputs, training=False)
return {
'logits': logits,
'predict_ids': tf.argmax(logits, axis=-1, output_type=tf.int32)
}
def validation_step(self,
inputs,
model: tf_keras.Model,
metrics=None) -> Mapping[str, tf.Tensor]:
"""Validatation step.
Args:
inputs: a dictionary of input tensors.
model: the keras.Model.
metrics: a nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
features, labels = inputs
outputs = self.inference_step(features, model)
loss = self.build_losses(labels=labels, model_outputs=outputs['logits'])
# Negative label ids are padding labels which should be ignored.
real_label_index = tf.where(tf.greater_equal(labels, 0))
predict_ids = tf.gather_nd(outputs['predict_ids'], real_label_index)
label_ids = tf.gather_nd(labels, real_label_index)
return {
self.loss: loss,
'predict_ids': predict_ids,
'label_ids': label_ids,
}
def aggregate_logs(self,
state=None,
step_outputs=None) -> Mapping[Text, List[List[Text]]]:
"""Aggregates over logs returned from a validation step."""
if state is None:
state = {'predict_class': [], 'label_class': []}
def id_to_class_name(batched_ids):
class_names = []
for per_example_ids in batched_ids:
class_names.append([])
for per_token_id in per_example_ids.numpy().tolist():
class_names[-1].append(self.task_config.class_names[per_token_id])
return class_names
# Convert id to class names, because `seqeval_metrics` relies on the class
# name to decide IOB tags.
state['predict_class'].extend(id_to_class_name(step_outputs['predict_ids']))
state['label_class'].extend(id_to_class_name(step_outputs['label_ids']))
return state
def reduce_aggregated_logs(self,
aggregated_logs,
global_step=None) -> Mapping[Text, float]:
"""Reduces aggregated logs over validation steps."""
label_class = aggregated_logs['label_class']
predict_class = aggregated_logs['predict_class']
return {
'f1':
seqeval_metrics.f1_score(label_class, predict_class),
'precision':
seqeval_metrics.precision_score(label_class, predict_class),
'recall':
seqeval_metrics.recall_score(label_class, predict_class),
'accuracy':
seqeval_metrics.accuracy_score(label_class, predict_class),
}
@exp_factory.register_config_factory('example_bert_classification_example')
def bert_classification_example() -> cfg.ExperimentConfig:
"""Return a minimum experiment config for Bert token classification."""
return cfg.ExperimentConfig(
task=ClassificationExampleConfig(),
trainer=cfg.TrainerConfig(
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adamw',
},
'learning_rate': {
'type': 'polynomial',
},
'warmup': {
'type': 'polynomial'
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
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