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classification_model.py
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classification_model.py
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# Copyright 2019 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.
"""Custom classification model that is already retained by data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow_examples.lite.model_maker.core.data_util import data_util
from tensorflow_examples.lite.model_maker.core.export_format import ExportFormat
from tensorflow_examples.lite.model_maker.core.task import custom_model
from tensorflow_examples.lite.model_maker.core.task import model_util
class ClassificationModel(custom_model.CustomModel):
""""The abstract base class that represents a Tensorflow classification model."""
DEFAULT_EXPORT_FORMAT = (ExportFormat.TFLITE, ExportFormat.LABEL)
ALLOWED_EXPORT_FORMAT = (ExportFormat.TFLITE, ExportFormat.LABEL,
ExportFormat.SAVED_MODEL, ExportFormat.TFJS)
def __init__(self, model_spec, index_to_label, shuffle, train_whole_model):
"""Initialize a instance with data, deploy mode and other related parameters.
Args:
model_spec: Specification for the model.
index_to_label: A list that map from index to label class name.
shuffle: Whether the data should be shuffled.
train_whole_model: If true, the Hub module is trained together with the
classification layer on top. Otherwise, only train the top
classification layer.
"""
super(ClassificationModel, self).__init__(model_spec, shuffle)
self.index_to_label = index_to_label
self.num_classes = len(index_to_label)
self.train_whole_model = train_whole_model
def evaluate(self, data, batch_size=32):
"""Evaluates the model.
Args:
data: Data to be evaluated.
batch_size: Number of samples per evaluation step.
Returns:
The loss value and accuracy.
"""
ds = data.gen_dataset(
batch_size, is_training=False, preprocess=self.preprocess)
return self.model.evaluate(ds)
def predict_top_k(self, data, k=1, batch_size=32):
"""Predicts the top-k predictions.
Args:
data: Data to be evaluated.
k: Number of top results to be predicted.
batch_size: Number of samples per evaluation step.
Returns:
top k results. Each one is (label, probability).
"""
if k < 0:
raise ValueError('K should be equal or larger than 0.')
ds = data.gen_dataset(
batch_size, is_training=False, preprocess=self.preprocess)
predicted_prob = self.model.predict(ds)
topk_prob, topk_id = tf.math.top_k(predicted_prob, k=k)
topk_label = np.array(self.index_to_label)[topk_id.numpy()]
label_prob = []
for label, prob in zip(topk_label, topk_prob.numpy()):
label_prob.append(list(zip(label, prob)))
return label_prob
def _export_labels(self, label_filepath):
if label_filepath is None:
raise ValueError("Label filepath couldn't be None when exporting labels.")
tf.compat.v1.logging.info('Saving labels in %s.', label_filepath)
with tf.io.gfile.GFile(label_filepath, 'w') as f:
f.write('\n'.join(self.index_to_label))
def evaluate_tflite(self, tflite_filepath, data):
"""Evaluates the tflite model.
Args:
tflite_filepath: File path to the TFLite model.
data: Data to be evaluated.
Returns:
The evaluation result of TFLite model - accuracy.
"""
ds = data.gen_dataset(
batch_size=1, is_training=False, preprocess=self.preprocess)
predictions, labels = [], []
lite_runner = model_util.get_lite_runner(tflite_filepath, self.model_spec)
for i, (feature, label) in enumerate(data_util.generate_elements(ds)):
log_steps = 1000
tf.compat.v1.logging.log_every_n(tf.compat.v1.logging.DEBUG,
'Processing example: #%d\n%s', log_steps,
i, feature)
probabilities = lite_runner.run(feature)
predictions.append(np.argmax(probabilities))
# Gets the ground-truth labels.
label = label[0]
if label.size > 1: # one-hot tensor.
label = np.argmax(label)
labels.append(label)
predictions, labels = np.array(predictions), np.array(labels)
result = {'accuracy': (predictions == labels).mean()}
return result