Is there a simpler way to handle batch inputs from tfrecords?
Question:
My question is about how to get batch inputs from multiple (or sharded) tfrecords. I’ve read the example https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410. The basic pipeline is, take the training set as as example, (1) first generate a series of tfrecords (e.g., train-000-of-005, train-001-of-005, …), (2) from these filenames, generate a list and fed them into the tf.train.string_input_producer to get a queue, (3) simultaneously generate a tf.RandomShuffleQueue to do other stuff, (4) using tf.train.batch_join to generate batch inputs.
I think this is complex, and I’m not sure the logic of this procedure. In my case, I have a list of .npy files, and I want to generate sharded tfrecords(multiple seperated tfrecords, not just one single large file). Each of these .npy files contains different number of positive and negative samples (2 classes). A basic method is to generate one single large tfrecord file. But the file is too large (~20Gb). So I resort to sharded tfrecords. Are there any simpler way to do this?
Answers:
The whole process is simplied using the Dataset API. Here are both the parts: (1): Convert numpy array to tfrecords and (2): read the tfrecords to generate batches.
1. Creation of tfrecords from a numpy array:
Example arrays:
inputs = np.random.normal(size=(5, 32, 32, 3))
labels = np.random.randint(0,2,size=(5,))
def npy_to_tfrecords(inputs, labels, filename):
with tf.io.TFRecordWriter(filename) as writer:
for X, y in zip(inputs, labels):
# Feature contains a map of string to feature proto objects
feature = {}
feature['X'] = tf.train.Feature(float_list=tf.train.FloatList(value=X.flatten()))
feature['y'] = tf.train.Feature(int64_list=tf.train.Int64List(value=[y]))
# Construct the Example proto object
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize the example to a string
serialized = example.SerializeToString()
# write the serialized objec to the disk
writer.write(serialized)
npy_to_tfrecords(inputs, labels, 'numpy.tfrecord')
2. Read the tfrecords using the Dataset API:
filenames = ['numpy.tfrecord']
dataset = tf.data.TFRecordDataset(filenames)
# for version 1.5 and above use tf.data.TFRecordDataset
# example proto decode
def _parse_function(example_proto):
keys_to_features = {'X':tf.io.FixedLenFeature(shape=(32, 32, 3), dtype=tf.float32),
'y': tf.io.FixedLenFeature((), tf.int64, default_value=0)}
parsed_features = tf.io.parse_single_example(example_proto, keys_to_features)
return parsed_features['X'], parsed_features['y']
# Parse the record into tensors.
dataset = dataset.map(_parse_function)
# Generate batches
dataset = dataset.batch(5)
Check the generated batches are proper:
for data in dataset:
break
np.testing.assert_allclose(inputs[0] ,data[0][0])
np.testing.assert_allclose(labels[0] ,data[1][0])
My question is about how to get batch inputs from multiple (or sharded) tfrecords. I’ve read the example https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410. The basic pipeline is, take the training set as as example, (1) first generate a series of tfrecords (e.g., train-000-of-005, train-001-of-005, …), (2) from these filenames, generate a list and fed them into the tf.train.string_input_producer to get a queue, (3) simultaneously generate a tf.RandomShuffleQueue to do other stuff, (4) using tf.train.batch_join to generate batch inputs.
I think this is complex, and I’m not sure the logic of this procedure. In my case, I have a list of .npy files, and I want to generate sharded tfrecords(multiple seperated tfrecords, not just one single large file). Each of these .npy files contains different number of positive and negative samples (2 classes). A basic method is to generate one single large tfrecord file. But the file is too large (~20Gb). So I resort to sharded tfrecords. Are there any simpler way to do this?
The whole process is simplied using the Dataset API. Here are both the parts: (1): Convert numpy array to tfrecords and (2): read the tfrecords to generate batches.
1. Creation of tfrecords from a numpy array:
Example arrays:
inputs = np.random.normal(size=(5, 32, 32, 3))
labels = np.random.randint(0,2,size=(5,))
def npy_to_tfrecords(inputs, labels, filename):
with tf.io.TFRecordWriter(filename) as writer:
for X, y in zip(inputs, labels):
# Feature contains a map of string to feature proto objects
feature = {}
feature['X'] = tf.train.Feature(float_list=tf.train.FloatList(value=X.flatten()))
feature['y'] = tf.train.Feature(int64_list=tf.train.Int64List(value=[y]))
# Construct the Example proto object
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize the example to a string
serialized = example.SerializeToString()
# write the serialized objec to the disk
writer.write(serialized)
npy_to_tfrecords(inputs, labels, 'numpy.tfrecord')
2. Read the tfrecords using the Dataset API:
filenames = ['numpy.tfrecord']
dataset = tf.data.TFRecordDataset(filenames)
# for version 1.5 and above use tf.data.TFRecordDataset
# example proto decode
def _parse_function(example_proto):
keys_to_features = {'X':tf.io.FixedLenFeature(shape=(32, 32, 3), dtype=tf.float32),
'y': tf.io.FixedLenFeature((), tf.int64, default_value=0)}
parsed_features = tf.io.parse_single_example(example_proto, keys_to_features)
return parsed_features['X'], parsed_features['y']
# Parse the record into tensors.
dataset = dataset.map(_parse_function)
# Generate batches
dataset = dataset.batch(5)
Check the generated batches are proper:
for data in dataset:
break
np.testing.assert_allclose(inputs[0] ,data[0][0])
np.testing.assert_allclose(labels[0] ,data[1][0])