Writing and Reading lists to TFRecord example - tensorflow

I want to write a list of integers (or any multidimensional numpy matrix) to one TFRecords example. For both a single value or a list of multiple values I can creates the TFRecord file without error. I know also how to read the single value back from TFRecord file as shown in the below code sample I compiled from various sources.
# Making an example TFRecord
my_example = tf.train.Example(features=tf.train.Features(feature={
'my_ints': tf.train.Feature(int64_list=tf.train.Int64List(value=[5]))
}))
my_example_str = my_example.SerializeToString()
with tf.python_io.TFRecordWriter('my_example.tfrecords') as writer:
writer.write(my_example_str)
# Reading it back via a Dataset
featuresDict = {'my_ints': tf.FixedLenFeature([], dtype=tf.int64)}
def parse_tfrecord(example):
features = tf.parse_single_example(example, featuresDict)
return features
Dataset = tf.data.TFRecordDataset('my_example.tfrecords')
Dataset = Dataset.map(parse_tfrecord)
iterator = Dataset.make_one_shot_iterator()
with tf.Session() as sess:
print(sess.run(iterator.get_next()))
But how can I read back a list of values (e.g. [5,6]) from one example? The featuresDict defines the feature to be of type int64, and it fails when I have multiple values in it and I get below error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Key: my_ints. Can't parse serialized Example.

You can achieve this by using tf.train.SequenceExample. I've edited your code to return both 1D and 2D data. First, you create a list of features which you place in a tf.train.FeatureList. We convert our 2D data to bytes.
vals = [5, 5]
vals_2d = [np.zeros((5,5), dtype=np.uint8), np.ones((5,5), dtype=np.uint8)]
features = [tf.train.Feature(int64_list=tf.train.Int64List(value=[val])) for val in vals]
features_2d = [tf.train.Feature(bytes_list=tf.train.BytesList(value=[val.tostring()])) for val in vals_2d]
featureList = tf.train.FeatureList(feature=features)
featureList_2d = tf.train.FeatureList(feature=features_2d)
In order to get the correct shape of our 2D feature we need to provide context (non-sequential data), this is done with a context dictionary.
context_dict = {'height': tf.train.Feature(int64_list=tf.train.Int64List(value=[vals_2d[0].shape[0]])),
'width': tf.train.Feature(int64_list=tf.train.Int64List(value=[vals_2d[0].shape[1]])),
'length': tf.train.Feature(int64_list=tf.train.Int64List(value=[len(vals_2d)]))}
Then you place each FeatureList in a tf.train.FeatureLists dictionary. Finally, this is placed in a tf.train.SequenceExample along with the context dictionary
my_example = tf.train.SequenceExample(feature_lists=tf.train.FeatureLists(feature_list={'1D':featureList,
'2D': featureList_2d}),
context = tf.train.Features(feature=context_dict))
my_example_str = my_example.SerializeToString()
with tf.python_io.TFRecordWriter('my_example.tfrecords') as writer:
writer.write(my_example_str)
To read it back into tensorflow you need to use tf.FixedLenSequenceFeature for the sequential data and tf.FixedLenFeature for the context data. We convert the bytes back to integers and we parse the context data in order to restore the correct shape.
# Reading it back via a Dataset
featuresDict = {'1D': tf.FixedLenSequenceFeature([], dtype=tf.int64),
'2D': tf.FixedLenSequenceFeature([], dtype=tf.string)}
contextDict = {'height': tf.FixedLenFeature([], dtype=tf.int64),
'width': tf.FixedLenFeature([], dtype=tf.int64),
'length':tf.FixedLenFeature([], dtype=tf.int64)}
def parse_tfrecord(example):
context, features = tf.parse_single_sequence_example(
example,
sequence_features=featuresDict,
context_features=contextDict
)
height = context['height']
width = context['width']
seq_length = context['length']
vals = features['1D']
vals_2d = tf.decode_raw(features['2D'], tf.uint8)
vals_2d = tf.reshape(vals_2d, [seq_length, height, width])
return vals, vals_2d
Dataset = tf.data.TFRecordDataset('my_example.tfrecords')
Dataset = Dataset.map(parse_tfrecord)
iterator = Dataset.make_one_shot_iterator()
with tf.Session() as sess:
print(sess.run(iterator.get_next()))
This will output the sequence of [5, 5] and the 2D numpy arrays. This blog post has a more in depth look at defining sequences with tfrecords https://dmolony3.github.io/Working%20with%20image%20sequences.html

Related

preprocess images with tf.data.experimental.make_csv_dataset or with read_csv option

I am adding this summarization of my issue to make it easier to understand:
I want to do exactly what is done in the following tensorflow example:
https://www.tensorflow.org/guide/datasets
# Reads an image from a file, decodes it into a dense tensor, and resizes it
# to a fixed shape.
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string)
image_resized = tf.image.resize_images(image_decoded, [28, 28])
return image_resized, label
# A vector of filenames.
filenames = tf.constant(["/var/data/image1.jpg", "/var/data/image2.jpg", ...])
# `labels[i]` is the label for the image in `filenames[i].
labels = tf.constant([0, 37, ...])
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(_parse_function)
The only differences are: I read the data from CSV that has many more features and then I call the map method:
dataset = tf.data.experimental.make_csv_dataset(file_pattern=CSV_PATH_TRAIN,
batch_size=2,
header=True,
label_name = 'label').map(_parse_function)
How does my _parse_function need to look like? How do I access the image path features, updates it to be an image presentation and return a modified numeric matrix feature of the image without changing anything at the other features?
thanks,
eilalan
==================Here are my code tries:==================
My code reads a CSV with feature columns and label. One of the features is image path, the others are strings.
The image path need to be processed into image numbers matrix.
I have tried doing so with the following options. In both ways tf.read_file fails with the input dimension error.
My question is how to pass one image at a time into the map methods
def read_image_png_option_1(image_path, depth=3, scale=False):
"""Reads the image from image_path (tf.string tensor) [jpg image].
Cast the result to float32 and if scale=True scale it in [-1,1]
using scale_image. Otherwise the values are in [0,1]
Reuturn:
the decoded jpeg image, casted to float32
"""
image = tf.image.convert_image_dtype(
tf.image.decode_png(tf.read_file(image_path), channels=depth),
dtype=tf.float32)
if scale:
image = scale_image(image)
return image
def read_image_png_option_2(features, depth=3, scale=False):
"""Reads the image from image_path (tf.string tensor) [jpg image].
Cast the result to float32 and if scale=True scale it in [-1,1]
using scale_image. Otherwise the values are in [0,1]
Reuturn:
the decoded jpeg image, casted to float32
"""
image = tf.image.convert_image_dtype(
tf.image.decode_png(tf.read_file(features['image']), channels=depth),
dtype=tf.float32)
if scale:
image = scale_image(image)
features['image'] = image
return features
def make_input_fn(fileName,batch_size=8, perform_shuffle=True):
"""An input function for training """
def _input_fn():
def decode_csv(line):
print('line is ',line)
filename_col,label_col,gender_col,ethinicity = tf.decode_csv(line,
[[""]]*amount_of_columns_csv,
field_delim=",",
na_value='NA',
select_cols=None)
image_col = read_image_png_option_1(filename_col)
d = dict(zip(['image','label','gender','ethinicity'], [image_col,label_col,gender_col,ethinicity])), label
return d
## OPTION 1:
# filenames could be more than one
# dataset = tf.data.TextLineDataset(filenames=fileName).skip(1).batch(batch_size).map(decode_csv)
## OPTION 2:
dataset = tf.data.experimental.make_csv_dataset(file_pattern=CSV_PATH_TRAIN,
batch_size=2,
header=True,
label_name = 'label').map(read_image_png_option_2)
#select_columns=[0,1]) #[tf.string,tf.string,tf.string,tf.string])
if perform_shuffle:
dataset = dataset.shuffle(buffer_size=256)
return dataset
return _input_fn()
train_input_fn = lambda: make_input_fn(CSV_PATH_TRAIN)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=50)
eval_input_fn = lambda: make_input_fn(CSV_PATH_VAL)
eval_spec = tf.estimator.EvalSpec(eval_input_fn)
feature_columns = [tf.feature_column.numeric_column("image",shape=(224,224)), # here i need a pyhton method to transform
tf.feature_column.categorical_column_with_vocabulary_list("gender", ["ww","ee"]),
tf.feature_column.categorical_column_with_vocabulary_list("ethinicity",["xx","yy"])]
estimator = tf.estimator.DNNClassifier(feature_columns=feature_columns,hidden_units=[1024, 512, 256],warm_start_from=ws)
tf.estimator.train_and_evaluate(estimator, train_spec=train_spec, eval_spec=eval_spec)
Error for option 2:
ValueError: Shape must be rank 0 but is rank 1 for 'ReadFile' (op: 'ReadFile') with input shapes: [2].
Error for option 1:
ValueError: Shape must be rank 0 but is rank 1 for 'ReadFile' (op: 'ReadFile') with input shapes: [?].
Any help is appreciated.
Thanks
First you need to read the CSV file into dataset.
Then for each row in your CSV you can call your parse function.
def getInput(fileList):
# returns a dataset containing list of filenames
files = tf.data.Dataset.from_tensor_slices(fileList)
# Returs a dataset containing list of rows taken from all the files in file list.
# dataset is filled dynamically and not all entries are read at once
dataset = files.interleave(tf.data.TextLineDataset)
# call parse function for each row
# returned dataset will contain list of whatever the parse function is returning for the row
# we want the image path to be converted to decoded image in parse function
dataset = dataset.map(_parse_function, num_parallel_calls=8)
# return an iterator for the dataset which will be used to get elements.
return dataset.make_one_shot_iterator().get_next()
The parse function will be passed only one parameter that will be a single row from the CSV file. You need to decode the CSV and do further processing on each value.
Let's say you have 3 columns in your CSV each being a string.
def _parse_function(value):
columns_default = [[""], [""], [""]]
# this will be a tensor of columns in the row
columns = tf.decode_csv(value, record_defaults=columns_default,
field_delim=',')
col_names = ["label", "imagepath", "c3"]
features = dict(zip(col_names, columns))
for f, tensor in features.items():
# process imagepath to decoded image
if f == "imagepath":
image_string = tf.read_file(tensor)
image_decoded = tf.image.decode_jpeg(image_string)
image_resized = tf.image.resize_images(image_decoded, [28, 28])
features[f] = image_resized
labels = tf.equal(features.pop('label'), "1")
labels = tf.expand_dims(labels, 0)
return features, labels
Edit:
Explanation for comment:
Dataset object simply contains a list of elements. The elements can be tensors or a tuple of tensors etc. Tensor object can contain anything. It could represent a single feature, a single record or a batch of record. Further dataset API provide handy methods to manipulate the elements within.
If you are using dataset with another API like estimator then they expect the dataset elements to be in specific format which is what need to return from our input function for eg.
https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#train
I have edited my code block above to describe what dataset object at each step will contain.
From what I understand is that you have image path as one of the field in your CSV and you want to convert that path into an actual decoded image which you will use as one of the feature.
Since the image is going to be just one of the feature, you should not try to create a dataset using image files alone. Dataset object will include all your features at once.
So doing this would be incorrect:
files = tf.data.Dataset.from_tensor_slices(ds['imagepath'])
dataset = files.interleave(tf.data.TextLineDataset)
If you are using make_csv() function to read your csv then it will convert each row of your csv into one record where one record will contain list of all features, same as columns of csv.
So each element in the returned dataset should contain a single tensor containing all your features.
Here your image path will be one of the features. now you want to transform that image path to decoded image.
I suppose you can do it by applying a parse function to elements of dataset using map() function but it will be slightly tricky as all your features are already packed inside a single tensor.

How to read (decode) tfrecords with tf.data API

I have a custom dataset, that I then stored as tfrecord, doing
# toy example data
label = np.asarray([[1,2,3],
[4,5,6]]).reshape(2, 3, -1)
sample = np.stack((label + 200).reshape(2, 3, -1))
def bytes_feature(values):
"""Returns a TF-Feature of bytes.
Args:
values: A string.
Returns:
A TF-Feature.
"""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
def labeled_image_to_tfexample(sample_binary_string, label_binary_string):
return tf.train.Example(features=tf.train.Features(feature={
'sample/image': bytes_feature(sample_binary_string),
'sample/label': bytes_feature(label_binary_string)
}))
def _write_to_tf_record():
with tf.Graph().as_default():
image_placeholder = tf.placeholder(dtype=tf.uint16)
encoded_image = tf.image.encode_png(image_placeholder)
label_placeholder = tf.placeholder(dtype=tf.uint16)
encoded_label = tf.image.encode_png(image_placeholder)
with tf.python_io.TFRecordWriter("./toy.tfrecord") as writer:
with tf.Session() as sess:
feed_dict = {image_placeholder: sample,
label_placeholder: label}
# Encode image and label as binary strings to be written to tf_record
image_string, label_string = sess.run(fetches=(encoded_image, encoded_label),
feed_dict=feed_dict)
# Define structure of what is going to be written
file_structure = labeled_image_to_tfexample(image_string, label_string)
writer.write(file_structure.SerializeToString())
return
However I cannot read it. First I tried (based on http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html , https://medium.com/coinmonks/storage-efficient-tfrecord-for-images-6dc322b81db4 and https://medium.com/mostly-ai/tensorflow-records-what-they-are-and-how-to-use-them-c46bc4bbb564)
def read_tfrecord_low_level():
data_path = "./toy.tfrecord"
filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
reader = tf.TFRecordReader()
_, raw_records = reader.read(filename_queue)
decode_protocol = {
'sample/image': tf.FixedLenFeature((), tf.int64),
'sample/label': tf.FixedLenFeature((), tf.int64)
}
enc_example = tf.parse_single_example(raw_records, features=decode_protocol)
recovered_image = enc_example["sample/image"]
recovered_label = enc_example["sample/label"]
return recovered_image, recovered_label
I also tried variations casting enc_example and decoding it, such as in Unable to read from Tensorflow tfrecord file However when I try to evaluate them my python session just freezes and gives no output or traceback.
Then I tried using eager execution to see what is happening, but apparently it is only compatible with tf.data API. However as far as I understand transformations on tf.data API are made on the whole dataset. https://www.tensorflow.org/api_guides/python/reading_data mentions that a decode function must be written, but doesn't give an example on how to do that. All the tutorials I have found are made for TFRecordReader (which doesn't work for me).
Any help (pinpointing what I am doing wrong/ explaining what is happening/ indications on how to decode tfrecords with tf.data API) is highly appreciated.
According to https://www.youtube.com/watch?v=4oNdaQk0Qv4 and https://www.youtube.com/watch?v=uIcqeP7MFH0 tf.data is the best way to create input pipelines, so I am highly interested on learning that way.
Thanks in advance!
I am not sure why storing the encoded png causes the evaluation to not work, but here is a possible way of working around the problem. Since you mentioned that you would like to use the tf.data way of creating input pipelines, I'll show how to use it with your toy example:
label = np.asarray([[1,2,3],
[4,5,6]]).reshape(2, 3, -1)
sample = np.stack((label + 200).reshape(2, 3, -1))
First, the data has to be saved to the TFRecord file. The difference from what you did is that the image is not encoded to png.
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
writer = tf.python_io.TFRecordWriter("toy.tfrecord")
example = tf.train.Example(features=tf.train.Features(feature={
'label_raw': _bytes_feature(tf.compat.as_bytes(label.tostring())),
'sample_raw': _bytes_feature(tf.compat.as_bytes(sample.tostring()))}))
writer.write(example.SerializeToString())
writer.close()
What happens in the code above is that the arrays are turned into strings (1d objects) and then stored as bytes features.
Then, to read the data back using the tf.data.TFRecordDataset and tf.data.Iterator class:
filename = 'toy.tfrecord'
# Create a placeholder that will contain the name of the TFRecord file to use
data_path = tf.placeholder(dtype=tf.string, name="tfrecord_file")
# Create the dataset from the TFRecord file
dataset = tf.data.TFRecordDataset(data_path)
# Use the map function to read every sample from the TFRecord file (_read_from_tfrecord is shown below)
dataset = dataset.map(_read_from_tfrecord)
# Create an iterator object that enables you to access all the samples in the dataset
iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes)
label_tf, sample_tf = iterator.get_next()
# Similarly to tf.Variables, the iterators have to be initialised
iterator_init = iterator.make_initializer(dataset, name="dataset_init")
with tf.Session() as sess:
# Initialise the iterator passing the name of the TFRecord file to the placeholder
sess.run(iterator_init, feed_dict={data_path: filename})
# Obtain the images and labels back
read_label, read_sample = sess.run([label_tf, sample_tf])
The function _read_from_tfrecord() is:
def _read_from_tfrecord(example_proto):
feature = {
'label_raw': tf.FixedLenFeature([], tf.string),
'sample_raw': tf.FixedLenFeature([], tf.string)
}
features = tf.parse_example([example_proto], features=feature)
# Since the arrays were stored as strings, they are now 1d
label_1d = tf.decode_raw(features['label_raw'], tf.int64)
sample_1d = tf.decode_raw(features['sample_raw'], tf.int64)
# In order to make the arrays in their original shape, they have to be reshaped.
label_restored = tf.reshape(label_1d, tf.stack([2, 3, -1]))
sample_restored = tf.reshape(sample_1d, tf.stack([2, 3, -1]))
return label_restored, sample_restored
Instead of hard-coding the shape [2, 3, -1], you could also store that too into the TFRecord file, but for simplicity I didn't do it.
I made a little gist with a working example.
Hope this helps!

How to perform data augmentation in Tensorflow Estimator's input_fn

Using Tensorflow's Estimator API, at what point in the pipeline should I perform the data augmentation?
According to this official Tensorflow guide, one place to perform the data augmentation is in the input_fn:
def parse_fn(example):
"Parse TFExample records and perform simple data augmentation."
example_fmt = {
"image": tf.FixedLengthFeature((), tf.string, ""),
"label": tf.FixedLengthFeature((), tf.int64, -1)
}
parsed = tf.parse_single_example(example, example_fmt)
image = tf.image.decode_image(parsed["image"])
# augments image using slice, reshape, resize_bilinear
# |
# |
# |
# v
image = _augment_helper(image)
return image, parsed["label"]
def input_fn():
files = tf.data.Dataset.list_files("/path/to/dataset/train-*.tfrecord")
dataset = files.interleave(tf.data.TFRecordDataset)
dataset = dataset.map(map_func=parse_fn)
# ...
return dataset
My question
If I perform data augmentation inside input_fn, does parse_fn return a single example or a batch including the original input image + all of the augmented variants? If it should only return a single [augmented] example, how do I ensure that all images in the dataset are used in its un-augmented form, as well as all variants?
If you use iterators on your dataset, your _augment_helper function will be called with each iteration of the dataset across each block of data fed in ( as you are calling the parse_fn in dataset.map )
Change your code to
ds_iter = dataset.make_one_shot_iterator()
ds_iter = ds_iter.get_next()
return ds_iter
I've tested this with a simple augmentation function
def _augment_helper(image):
print(image.shape)
image = tf.image.random_brightness(image,255.0, 1)
image = tf.clip_by_value(image, 0.0, 255.0)
return image
Change 255.0 to whatever the maximum value is in your dataset, I used 255.0 as my example's data set was in 8 bit pixel values
It will return single examples for every call you make to the parse_fn, then if you use the .batch() operation it will return a batch of parsed images

TensorFlow input function for reading sparse data (in libsvm format)

I'm new to TensorFlow and trying to use the Estimator API for some simple classification experiments. I have a sparse dataset in libsvm format. The following input function works for small datasets:
def libsvm_input_function(file):
def input_function():
indexes_raw = []
indicators_raw = []
values_raw = []
labels_raw = []
i=0
for line in open(file, "r"):
data = line.split(" ")
label = int(data[0])
for fea in data[1:]:
id, value = fea.split(":")
indexes_raw.append([i,int(id)])
indicators_raw.append(int(1))
values_raw.append(float(value))
labels_raw.append(label)
i=i+1
indexes = tf.SparseTensor(indices=indexes_raw,
values=indicators_raw,
dense_shape=[i, num_features])
values = tf.SparseTensor(indices=indexes_raw,
values=values_raw,
dense_shape=[i, num_features])
labels = tf.constant(labels_raw, dtype=tf.int32)
return {"indexes": indexes, "values": values}, labels
return input_function
However, for a dataset of a few GB size I get the following error:
ValueError: Cannot create a tensor proto whose content is larger than 2GB.
How can I avoid this error? How should I write an input function to read medium-sized sparse datasets (in libsvm format)?
When use estimator, for libsvm data input, you can create dense index list, dense value list, then use feature_column.categorical_column_with_identity and feature_column.weighted_categorical_column to create feature column, finally, put feature columns to estimator. Maybe your input features length is variable, you can use padded_batch to handle it.
here some codes:
## here is input_fn
def input_fn(data_dir, is_training, batch_size):
def parse_csv(value):
## here some process to create feature_indices list, feature_values list and labels
return {"index": feature_indices, "value": feature_values}, labels
dataset = tf.data.Dataset.from_tensor_slices(your_filenames)
ds = dataset.flat_map(
lambda f: tf.data.TextLineDataset(f).map(parse_csv)
)
ds = ds.padded_batch(batch_size, ds.output_shapes, padding_values=(
{
"index": tf.constant(-1, dtype=tf.int32),
"value": tf.constant(0, dtype=tf.float32),
},
tf.constant(False, dtype=tf.bool)
))
return ds.repeat().prefetch(batch_size)
## create feature column
def build_model_columns():
categorical_column = tf.feature_column.categorical_column_with_identity(
key='index', num_buckets=your_feature_dim)
sparse_columns = tf.feature_column.weighted_categorical_column(
categorical_column=categorical_column, weight_feature_key='value')
dense_columns = tf.feature_column.embedding_column(sparse_columns, your_embedding_dim)
return [sparse_columns], [dense_columns]
## when created feature column, you can put them into estimator, eg. put dense_columns into DNN, and sparse_columns into linear model.
## for export savedmodel
def raw_serving_input_fn():
feature_spec = {"index": tf.placeholder(shape=[None, None], dtype=tf.int32),
"value": tf.placeholder(shape=[None, None], dtype=tf.float32)}
return tf.estimator.export.build_raw_serving_input_receiver_fn(feature_spec)
Another way, you can create your custom feature column, like this: _SparseArrayCategoricalColumn
I have been using tensorflow.contrib.libsvm. Here's an example (i am using eager execution with generators)
import os
import tensorflow as tf
import tensorflow.contrib.libsvm as libsvm
def all_libsvm_files(folder_path):
for file in os.listdir(folder_path):
if file.endswith(".libsvm"):
yield os.path.join(folder_path, file)
def load_libsvm_dataset(path_to_folder):
return tf.data.TextLineDataset(list(all_libsvm_files(path_to_folder)))
def libsvm_iterator(path_to_folder):
dataset = load_libsvm_dataset(path_to_folder)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
yield libsvm.decode_libsvm(tf.reshape(next_element, (1,)),
num_features=666,
dtype=tf.float32,
label_dtype=tf.float32)
libsvm_iterator gives you a feature-label pair back on each iteration, from multiple files inside a folder that you specify.

tensorflow record with float numpy array

I want to create tensorflow records to feed my model;
so far I use the following code to store uint8 numpy array to TFRecord format;
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _floats_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def convert_to_record(name, image, label, map):
filename = os.path.join(params.TRAINING_RECORDS_DATA_DIR, name + '.' + params.DATA_EXT)
writer = tf.python_io.TFRecordWriter(filename)
image_raw = image.tostring()
map_raw = map.tostring()
label_raw = label.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'image_raw': _bytes_feature(image_raw),
'map_raw': _bytes_feature(map_raw),
'label_raw': _bytes_feature(label_raw)
}))
writer.write(example.SerializeToString())
writer.close()
which I read with this example code
features = tf.parse_single_example(example, features={
'image_raw': tf.FixedLenFeature([], tf.string),
'map_raw': tf.FixedLenFeature([], tf.string),
'label_raw': tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape(params.IMAGE_HEIGHT*params.IMAGE_WIDTH*3)
image = tf.reshape(image_, (params.IMAGE_HEIGHT,params.IMAGE_WIDTH,3))
map = tf.decode_raw(features['map_raw'], tf.uint8)
map.set_shape(params.MAP_HEIGHT*params.MAP_WIDTH*params.MAP_DEPTH)
map = tf.reshape(map, (params.MAP_HEIGHT,params.MAP_WIDTH,params.MAP_DEPTH))
label = tf.decode_raw(features['label_raw'], tf.uint8)
label.set_shape(params.NUM_CLASSES)
and that's working fine. Now I want to do the same with my array "map" being a float numpy array, instead of uint8, and I could not find examples on how to do it;
I tried the function _floats_feature, which works if I pass a scalar to it, but not with arrays;
with uint8 the serialization can be done by the method tostring();
How can I serialize a float numpy array and how can I read that back?
FloatList and BytesList expect an iterable. So you need to pass it a list of floats. Remove the extra brackets in your _float_feature, ie
def _floats_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
numpy_arr = np.ones((3,)).astype(np.float)
example = tf.train.Example(features=tf.train.Features(feature={"bytes": _floats_feature(numpy_arr)}))
print(example)
features {
feature {
key: "bytes"
value {
float_list {
value: 1.0
value: 1.0
value: 1.0
}
}
}
}
I will expand on the Yaroslav's answer.
Int64List, BytesList and FloatList expect an iterator of the underlying elements (repeated field). In your case you can use a list as an iterator.
You mentioned: it works if I pass a scalar to it, but not with arrays. And this is expected, because when you pass a scalar, your _floats_feature creates an array of one float element in it (exactly as expected). But when you pass an array you create a list of arrays and pass it to a function which expects a list of floats.
So just remove construction of the array from your function: float_list=tf.train.FloatList(value=value)
I've stumbled across this while working on a similar problem. Since part of the original question was how to read back the float32 feature from tfrecords, I'll leave this here in case it helps anyone:
If map.ravel() was used to input map of dimensions [x, y, z] into _floats_feature:
features = {
...
'map': tf.FixedLenFeature([x, y, z], dtype=tf.float32)
...
}
parsed_example = tf.parse_single_example(serialized=serialized, features=features)
map = parsed_example['map']
Yaroslav's example failed when a nd array was the input:
numpy_arr = np.ones((3,3)).astype(np.float)
I found that it worked when I used numpy_arr.ravel() as the input. But is there a better way to do it?
First of all, many thanks to Yaroslav and Salvador for their enlightening answers.
According to my experience, their methods only works when the input is a 1D NumPy array as the size of (n, ). When the input is a Numpy array with the dimension of more than 2, the following error info appears:
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
numpy_arr = np.arange(12).reshape(2, 2, 3).astype(np.float)
example = tf.train.Example(features=tf.train.Features(feature={"bytes":
_float_feature(numpy_arr)}))
print(example)
TypeError: array([[0., 1., 2.],
[3., 4., 5.]]) has type numpy.ndarray, but expected one of: int, long, float
So, I'd like to expand on Tsuan's answer, that is, flattening the input before it was fed into the TF example. The modified code is as follows:
def _floats_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
numpy_arr = np.arange(12).reshape(2, 2, 3).astype(np.float).flatten()
example = tf.train.Example(features=tf.train.Features(feature={"bytes":
_float_feature(numpy_arr)}))
print(example)
In addition, np.flatten() is more applicable than np.ravel().
Use tfrmaker, a TFRecord utility package. You can install the package with pip:
pip install tfrmaker
Then you could create tfrecords like this:
from tfrmaker import images
# mapping label names with integer encoding.
LABELS = {"bishop": 0, "knight": 1, "pawn": 2, "queen": 3, "rook": 4}
# specifiying data and output directories.
DATA_DIR = "datasets/chess/"
OUTPUT_DIR = "tfrecords/chess/"
# create tfrecords from the images present in the given data directory.
info = images.create(DATA_DIR, LABELS, OUTPUT_DIR)
# info contains a list of information (path: releative path, size: no of images in the tfrecord) about created tfrecords
print(info)
The package also has some cool features like:
dynamic resizing
splitting tfrecords into optimal shards
spliting training, validation, testing of tfrecords
count no of images in tfrecords
asynchronous tfrecord creation
NOTE: This package currently supports image datasets that are organised as directories with class names as sub directory names.