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07ef7ab | 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 | # Copyright 2024 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 _crop_and_resize(self, 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])
return image
def _build_inputs(self, image):
"""Builds classification model inputs for serving."""
# Center crops and resizes image.
if isinstance(image, tf.RaggedTensor):
image = image.to_tensor()
image = tf.cast(image, dtype=tf.float32)
# For these input types, decode_image already performs cropping.
if not (
self._input_type in ['tf_example', 'image_bytes']
and len(self._input_image_size) == 2):
image = self._crop_and_resize(image)
# 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 _decode_image(self, encoded_image_bytes: str) -> tf.Tensor:
"""Decodes an image bytes to an image tensor.
Use `tf.image.decode_image` to decode an image if input is expected to be 2D
image; otherwise use `tf.io.decode_raw` to convert the raw bytes to tensor
and reshape it to desire shape.
Args:
encoded_image_bytes: An encoded image string to be decoded.
Returns:
A decoded image tensor.
"""
if len(self._input_image_size) == 2:
# Decode an image if 2D input is expected.
image_tensor = tf.image.decode_image(
encoded_image_bytes, channels=self._num_channels
)
image_tensor.set_shape((None, None, self._num_channels))
# Crop the image inside the same loop as decoding an image
# if there could be several images of different sizes in the batch.
image_tensor = tf.cast(image_tensor, dtype=tf.float32)
image_tensor = self._crop_and_resize(image_tensor)
image_tensor = tf.cast(image_tensor, tf.uint8)
return image_tensor
else:
# Convert raw bytes into a tensor and reshape it, if not 2D input.
image_tensor = tf.io.decode_raw(encoded_image_bytes, out_type=tf.uint8)
image_tensor = tf.reshape(
image_tensor, self._input_image_size + [self._num_channels]
)
return image_tensor
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.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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