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# 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.
"""Image classification input and model functions for serving/inference."""
import tensorflow as tf, tf_keras
from official.vision.modeling import factory
from official.vision.ops import preprocess_ops
from official.vision.serving import export_base
class ClassificationModule(export_base.ExportModule):
"""classification Module."""
def _build_model(self):
input_specs = tf_keras.layers.InputSpec(
shape=[self._batch_size] + self._input_image_size + [3])
return factory.build_classification_model(
input_specs=input_specs,
model_config=self.params.task.model,
l2_regularizer=None)
def _build_inputs(self, image):
"""Builds classification model inputs for serving."""
# Center crops and resizes image.
if self.params.task.train_data.aug_crop:
image = preprocess_ops.center_crop_image(image)
image = tf.image.resize(
image, self._input_image_size, method=tf.image.ResizeMethod.BILINEAR)
image = tf.reshape(
image, [self._input_image_size[0], self._input_image_size[1], 3])
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(
image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
return image
def serve(self, images):
"""Cast image to float and run inference.
Args:
images: uint8 Tensor of shape [batch_size, None, None, 3]
Returns:
Tensor holding classification output logits.
"""
# Skip image preprocessing when input_type is tflite so it is compatible
# with TFLite quantization.
if self._input_type != 'tflite':
with tf.device('cpu:0'):
images = tf.cast(images, dtype=tf.float32)
images = tf.nest.map_structure(
tf.identity,
tf.map_fn(
self._build_inputs,
elems=images,
fn_output_signature=tf.TensorSpec(
shape=self._input_image_size + [3], dtype=tf.float32),
parallel_iterations=32))
logits = self.inference_step(images)
if self.params.task.train_data.is_multilabel:
probs = tf.math.sigmoid(logits)
else:
probs = tf.nn.softmax(logits)
return {'logits': logits, 'probs': probs}
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