我在Github上关注HamedMP的ImageFlow项目，并在我自己的图像上创建一个tensorflow tfrecords文件。我的图片大小相同223x334x3。我在自己的图像上尝试了cifar示例。因为cifar convnet需要32x32大小的图像所以我必须调整它的大小。
我的功能是读取tfrecords文件，解码图像并调整大小如下：
def read_my_file_format(filename_queue):
reader = tf.TFRecordReader()
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
image = tf.cast(image, tf.float32)
label = tf.cast(features['label'], tf.int32)
image = tf.reshape(image, [223446])
image.set_shape(223446)
print 'get shape: ',image.get_shape()
print 'image: ',image.get_shape()
print 'label: ',label.get_shape()
image = tf.image.resize_images(image, 32,32)
image = tf.reshape(image,[32,32,3])
#processed_example = some_processing(example)
processed_example = image
return processed_example, label
但是当我打电话给它时，我收到了以下错误：
Last executed 2016-07-22 23:58:40 in 71ms
get shape: (223446,)
image: (223446,)
label: ()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-32-44cda3e31e6a> in <module>()
----> 1 tf.app.run()
/home/guanwanxian/anaconda2/envs/theano/lib/python2.7/site-packages/tensorflow/python/platform/app.pyc in run(main)
28 f._parse_flags()
29 main = main or sys.modules['__main__'].main
---> 30 sys.exit(main(sys.argv))
<ipython-input-7-2788f377abf1> in main(argv)
1 def main(argv=None):
----> 2 train()
<ipython-input-31-4c9a489f07f7> in train(re_train, continue_from_pre)
8 # Get images and labels for CIFAR-10.
9 # images, labels = my_input.inputs()
---> 10 images, labels = input_pipeline(['../data/trainV2.tfrecords'],batch_size=128)
11 val_images, val_labels = input_pipeline(['../data/testV2.tfrecords'],batch_size=128)
12
<ipython-input-3-ce4e93d1c196> in input_pipeline(filenames, batch_size, num_epochs)
2 filename_queue = tf.train.string_input_producer(
3 filenames, num_epochs=num_epochs, shuffle=True)
----> 4 example, label = read_my_file_format(filename_queue)
5 # min_after_dequeue defines how big a buffer we will randomly sample
6 # from -- bigger means better shuffling but slower start up and more
<ipython-input-30-8be4eeea8a06> in read_my_file_format(filename_queue)
21 print 'label: ',label.get_shape()
22
---> 23 image = tf.image.resize_images(image, 32,32)
24 image = tf.reshape(image,[32,32,3])
25
/home/guanwanxian/anaconda2/envs/theano/lib/python2.7/site-packages/tensorflow/python/ops/image_ops.pyc in resize_images(images, new_height, new_width, method, align_corners)
629 images = array_ops.expand_dims(images, 0)
630
--> 631 _, height, width, depth = _ImageDimensions(images)
632
633 # Handle tensor-valued sizes as well as Python integers.
ValueError: need more than 1 value to unpack
和我从图像创建tfrecords的代码几乎与HamedMP的相同，并进行了如下修改：
def read_image_and_labels_from(path):
'''read images and return image and label array
'''
directories = glob.glob(os.path.join(path,'*'))
class_names = [os.path.basename(directory) for directory in directories]
class_names.sort()
num_classes = len(class_names)
file_paths = glob.glob(os.path.join(path,'*/*'))
file_paths = sorted(file_paths,
key=lambda filename:os.path.basename(filename).split('.')[0])
images = []
labels = []
shapes = []
shape_set = set()
for filename in file_paths:
im = Image.open(filename)
arr = np.asarray(im, np.uint8)
images.append(arr)
im_shape = arr.shape
shapes.append(im_shape) # tuple of 3
if im_shape not in shape_set:
shape_set.add(im_shape)
# image_name = os.path.basename(filename).split('.')[0]
class_name = os.path.basename(os.path.dirname(filename))
label_num = class_names.index(class_name)
labels.append(np.asarray(label_num, np.uint32))
images = np.array(images)
labels = np.array(labels)
shapes = np.array(shapes)
print images.shape, labels.shape, shapes.shape
print 'shape set: ', shape_set
return images, labels, shapes
def convert_images_to_tfrecords_file(images, labels, shapes, directorys, name):
num_examples = labels.shape[0]
print('labels shape is: ', labels.shape[0])
if num_examples != images.shape[0] != labels.shape[0]:
raise ValueError("Images size %d does not match label size %d." %
(images.shape[0], num_examples))
filename = os.path.join(directorys, name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
image_raw = images[index].tostring()
image_shape = shapes[index]
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(image_shape[0]),
'width': _int64_feature(image_shape[1]),
'depth': _int64_feature(image_shape[2]),
'label': _int64_feature(int(labels[index])),
'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
我的代码出了什么问题？谢谢你的帮助