我正在使用deeplab框架来对具有4个以上信息通道的图像进行分类(Github：https://github.com/tensorflow/models/tree/master/research/deeplab 使用tensorflow版本1.14.0(。
我的想法是将单独的通道放入.gif文件中，并使用修改后的build_voc_2012.py和build_data.py版本以及修改后的data_generator.py来读取它们。其他所有内容都保留在存储库中。
分片生成和 train.py 似乎运行良好。在引发错误的 eval.py 中遇到此问题。
这是生成分片的代码。
"""Contains common utility functions and classes for building dataset.
This script contains utility functions and classes to converts dataset to
TFRecord file format with Example protos.
The Example proto contains the following fields:
image/encoded: encoded image content.
image/filename: image filename.
image/format: image file format.
image/height: image height.
image/width: image width.
image/channels: image channels.
image/segmentation/class/encoded: encoded semantic segmentation content.
image/segmentation/class/format: semantic segmentation file format.
"""
import collections
import six
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_enum('image_format', 'png', ['jpg', 'jpeg', 'png', 'gif'],
'Image format.')
tf.app.flags.DEFINE_enum('label_format', 'png', ['png'],
'Segmentation label format.')
# A map from image format to expected data format.
_IMAGE_FORMAT_MAP = {
'jpg': 'jpeg',
'jpeg': 'jpeg',
'png': 'png',
'gif': 'gif'
}
class ImageReader(object):
"""Helper class that provides TensorFlow image coding utilities."""
def __init__(self, image_format = "jpeg", channels=3):
"""Class constructor.
Args:
image_format: Image format. Only 'jpeg', 'jpg', or 'png' are supported.
channels: Image channels.
"""
with tf.Graph().as_default():
self._decode_data = tf.placeholder(dtype=tf.string)
self._image_format = image_format
self._session = tf.Session()
self.channels = channels
if self._image_format in ('jpeg', 'jpg'):
self._decode = tf.image.decode_jpeg(self._decode_data,channels)
elif self._image_format == 'png':
self._decode = tf.image.decode_png(self._decode_data,channels)
elif self._image_format == 'gif':
self._decode = tf.image.decode_gif(self._decode_data)
def read_image_dims_gif(self, gif_data):
"""Reads the image dimensions.
Args:
image_data: numpy array of image data.
Returns:
image_height and image_width.
"""
image = self.decode_gif(gif_data)
return image.shape[:4]
def decode_gif(self, image_data):
"""Decodes the image data string.
Args:
image_data: string of image data.
Returns:
Decoded image data.
Raises:
ValueError: Value of image channels not supported.
"""
image = self._session.run(self._decode,
feed_dict={self._decode_data: image_data})
return image
def _float64_list_feature(values):
"""Returns a TF-Feature of float_list.
Args:
values: A scalar or list of values.
Returns:
A TF-Feature.
"""
if not isinstance(values, collections.Iterable):
values = [values]
return tf.train.Feature(float_list=tf.train.FloatList(value=values))
def _int64_list_feature(values):
"""Returns a TF-Feature of int64_list.
Args:
values: A scalar or list of values.
Returns:
A TF-Feature.
"""
if not isinstance(values, collections.Iterable):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def _bytes_list_feature(values):
"""Returns a TF-Feature of bytes.
Args:
values: A string.
Returns:
A TF-Feature.
"""
def norm2bytes(value):
return value.encode() if isinstance(value, str) and six.PY3 else value
return tf.train.Feature(
bytes_list=tf.train.BytesList(value=[norm2bytes(values)]))
def image_seg_to_tfexample_gif(image_data, filename, height, width, seg_data, channels, frames):
"""Converts one image/segmentation pair to tf example.
Args
image_data: encoded image data
filename: image filename.
height: image height.
width: image width.
frames: number of frames in gif
seg_data: string of semantic segmentation data.
channels: int of number of image channels
Returns:
tf example of one image/segmentation pair.
"""
return tf.train.Example(features=tf.train.Features(feature={
'image/encoded': _bytes_list_feature(image_data),
'image/filename': _bytes_list_feature(filename),
'image/format': _bytes_list_feature(
_IMAGE_FORMAT_MAP[FLAGS.image_format]),
'image/height': _int64_list_feature(height),
'image/width': _int64_list_feature(width),
'image/channels': _float64_list_feature(channels),
'image/segmentation/class/encoded': (
_bytes_list_feature(seg_data)),
'image/segmentation/class/format': _bytes_list_feature(
FLAGS.label_format),
}))
eval.py 这段代码似乎产生了错误：
tf.contrib.training.evaluate_repeatedly(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
eval_ops=[update_op],
max_number_of_evaluations=num_eval_iters,
hooks=hooks,
eval_interval_secs=FLAGS.eval_interval_secs)
错误消息如下：
Traceback (most recent call last):
File "/home/user/models-master/research/deeplab/eval.py", line 188, in <module>
tf.app.run()
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/user/.local/lib/python2.7/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/user/.local/lib/python2.7/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/home/user/models-master/research/deeplab/eval.py", line 181, in main
eval_interval_secs=FLAGS.eval_interval_secs)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/contrib/training/python/training/evaluation.py", line 453, in evaluate_repeatedly
session.run(eval_ops, feed_dict)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 754, in run
run_metadata=run_metadata)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1252, in run
run_metadata=run_metadata)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1353, in run
raise six.reraise(*original_exc_info)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1338, in run
return self._sess.run(*args, **kwargs)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1411, in run
run_metadata=run_metadata)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1169, in run
return self._sess.run(*args, **kwargs)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1173, in _run
feed_dict_tensor, options, run_metadata)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1350, in _do_run
run_metadata)
File "/home/user/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Got 3 frames, but animated gifs can only be decoded by tf.image.decode_gif or tf.image.decode_image
[[{{node cond/else/_1/DecodePng}}]]
[[IteratorGetNext]]
(1) Invalid argument: Got 3 frames, but animated gifs can only be decoded by tf.image.decode_gif or tf.image.decode_image
[[{{node cond/else/_1/DecodePng}}]]
[[IteratorGetNext]]
[[mean_iou/confusion_matrix/assert_less_1/Assert/AssertGuard/Assert/data_1/_2007]]
0 successful operations.
0 derived errors ignored.
我的回购链接如下： https://github.com/michael-ross-scott/DeeplabV3
长话短说，我已经解决了我 eval.py 的问题，结果在data_generator.py中，有以下代码行试图解码我编码的.gif分片：
def _parse_function(self, example_proto):
"""Function to parse the example proto.
Args:
example_proto: Proto in the format of tf.Example.
Returns:
A dictionary with parsed image, label, height, width and image name.
Raises:
ValueError: Label is of wrong shape.
"""
def _decode_image(content, channels):
return tf.cond(
tf.image.is_jpeg(content),
lambda: tf.image.decode_jpeg(content, channels),
lambda: tf.image.decode_png(content, channels))
features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/filename':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/height':
tf.FixedLenFeature((), tf.int64, default_value=0),
'image/width':
tf.FixedLenFeature((), tf.int64, default_value=0),
'channels':
tf.FixedLenFeature((), tf.int64, default_value=3),
'image/segmentation/class/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/segmentation/class/format':
tf.FixedLenFeature((), tf.string, default_value='png'),
}
parsed_features = tf.parse_single_example(example_proto, features)
image = _decode_image(parsed_features['image/encoded'], channels=3)
label = None
if self.split_name != common.TEST_SET:
label = _decode_image(
parsed_features['image/segmentation/class/encoded'], channels=1)
我决定将decode_image解耦为两个函数：decode_image和decode_label。我只修改了图像和标签变量来调用适当的函数
def _decode_image(content, channels):
return tf.cond(
tf.image.is_jpeg(content),
lambda: tf.image.decode_jpeg(content, channels),
lambda: tf.image.decode_gif(content))
def _decode_label(content,channels):
return tf.cond(
tf.image.is_jpeg(content),
lambda: tf.image.decode_jpeg(content, channels),
lambda: tf.image.decode_png(content, channels))
image = _decode_image(parsed_features['image/encoded'], channels=3)
label = _decode_label(
parsed_features['image/segmentation/class/encoded'], channels=1)
唉，核心/实用程序中出现了另一个错误.py
(0) Invalid argument: input must be 4-dimensional[1,1,512,512,3]
[[node ResizeBilinear (defined at /models-master/research/deeplab/core/utils.py:34) ]]
[[IteratorGetNext]]
[[Mean_49/_6009]]
(1) Invalid argument: input must be 4-dimensional[1,1,512,512,3]
[[node ResizeBilinear (defined at /models-master/research/deeplab/core/utils.py:34) ]]
[[IteratorGetNext]]
0 successful operations.
0 derived errors ignored.
长话短说;如果不对这个代码库进行大量修改，就不可能修改 deeplabv3 以读取.gif文件。