As the titled state, only 1 image label is loaded from my tfrecord files. There is a variable number of images/labels in each tfrecord, but always at least 8 pairs. I am using TF version: 2.4.1
Possibly related I am getting this warning:
WARNING:tensorflow:AutoGraph could not transform <function parse_tfr_element at 0x7fbb7db99160> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux,
export AUTOGRAPH_VERBOSITY=10
) and attach the full output. Cause: module 'gast' has no attribute 'Index' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Below are the functions I use to load the test data. Any help is appreciated.
def parse_tfr_element(element):
data = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width':tf.io.FixedLenFeature([], tf.int64),
'depth':tf.io.FixedLenFeature([], tf.int64),
'raw_label':tf.io.FixedLenFeature([], tf.string),#tf.string = bytestring (not text string)
'raw_image' : tf.io.FixedLenFeature([], tf.string),#tf.string = bytestring (not text string)
}
content = tf.io.parse_single_example(element, data)
height = content['height']
width = content['width']
depth = content['depth']
raw_label = content['raw_label']
raw_image = content['raw_image']
#get our 'feature'-- our image -- and reshape it appropriately
feature = tf.io.parse_tensor(raw_image, out_type=tf.float16)
feature = tf.reshape(feature, shape=[height,width,depth])
label = tf.io.parse_tensor(raw_label, out_type=tf.int8)
label = tf.reshape(label, shape=[height,width])
return (feature, label)
def get_batched_dataset(filenames):
option_no_order = tf.data.Options()
option_no_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames)
dataset = dataset.with_options(option_no_order)
dataset = dataset.map(parse_tfr_element, num_parallel_calls=AUTO)
dataset = dataset.shuffle(2048)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
return dataset
CodePudding user response:
It turns out the problem was silly and had nothing to do with the functions I posted in the question. The problem was I had the following value for steps_per_epoch fed into the model.
steps_per_epoch = len(training_filenames) // BATCH_SIZE
since the files hold multiple cases, len(training_filenames) needs to be multiplied by the number of cases in each file.
steps_per_epoch = len(training_filenames) * images_in_file // BATCH_SIZE