tfrecord | TFRecord reader for PyTorch | Machine Learning library
kandi X-RAY | tfrecord Summary
kandi X-RAY | tfrecord Summary
This library allows reading and writing tfrecord files efficiently in python. The library also provides an IterableDataset reader of tfrecord files for PyTorch. Currently uncompressed and compressed gzip TFRecords are supported.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Load TFRecord loader
- Load examples from data_path
- Returns an iterator over TFRecord instances
- Load examples from examples
- Extract features from features
- Process a feature protobuf
- Close the file
- Sample the given iterators
- Cycle through an iterable
- Create a tfrecord index
- Serializes a TF sequence example
- Writes the data to the file
- Serialize a tf example
- Return the CRC of the given data
tfrecord Key Features
tfrecord Examples and Code Snippets
Lines of Code : 181License : Non-SPDX (Apache License 2.0)
Copydef make_batched_features_dataset_v2(file_pattern,
batch_size,
features,
reader=None,
label_key=None,
def _make_table_from_tfrecord_gzip_file(self, key_dtype, name):
dataset = readers.TFRecordDataset(
self.vocabulary_file, compression_type='GZIP')
def key_dtype_fn(key):
return key if key_dtype is dtypes.string else string_ops.s def write_to_tfrecord(label, shape, binary_image, tfrecord_file):
""" This example is to write a sample to TFRecord file. If you want to write
more samples, just use a loop.
"""
writer = tf.python_io.TFRecordWriter(tfrecord_file)
steps_per_epoch = len(training_filenames) // BATCH_SIZE
steps_per_epoch = len(training_filenames) * images_in_file // BATCH_SIZE
>>> ds = tf.data.Dataset.from_generator(lambda: np.arange(100), output_signature=tf.TensorSpec(shape=(), dtype=tf.int32))
>>> for d in ds:
... print(d)
...
tf.Tensor(0, shape=(), dtype=int32)
tf.Tensor(1, shape=(), dtdef transform_targets_for_output(y_true, grid_size, anchor_indices):
try:
n = tf.shape(y_true)[0]
y_true_out = tf.zeros((n, grid_size, grid_size, tf.shape(anchor_indices)[0], 6))
anchor_indices = tf.cast(anchor_indicedef rle_class_1(image_id):
temp_df = df['class_1_rle'][df['image_id'] == image_id]
for rle in temp_df:
rle_tensor = tf.constant(rle)
return rle_tensor.numpy()
paths_dict = dict(zip(file_idsdataset = get_dataset('test.tfrecords').batch(1)
from tensorflow.keras.layers import Conv3D, Conv3DTranspose, Input, Rescaling
num_classes = 4
my_model = tf.keras.Sequential([
Input(shape = (240, 240, 155, 4)),
UnimplementedError: File system scheme '[local]' not implemented (file: './data/temp/2692738424590406024')
Encountered when executing an operation using EagerExecutor. This error cancels all future operations and poisons their output ttrain = train.batch(32)
val = val.batch(32)
Community Discussions
Trending Discussions on tfrecord
QUESTION
I found a training dataset which is a set of tfrecords files,im trying to convert them into images but with no results,is it possible to convert them to images ?
...ANSWER
Answered 2022-Mar-11 at 08:15To find out what is inside a tf.record use tf.data.TFRecordDataset and tf.train.Example:
QUESTION
I am writing a function to save images to TFRecord files in order to then read then using the Data API of TensorFlow. However, when trying to create a TFRecord to save it, I receive the following error message:
...ANSWER
Answered 2022-Feb-22 at 11:13The problem is that image is a tensor but you need a list of float values. Try something like this:
QUESTION
You'll need this notebook to reproduce the error which downloads the files below and runs the exact same code following the description.
labels.csv: each row containsx0,y0,x1,y1text coordinates, and other columns not affecting the outcome.yolo-train-0.tfrecord: Contains 90% of the examples found inlabels.csv. Each example contains all labels/rows corresponding to the image in the example.
I'm experiencing a recurring error that happens when iterating over a tfrecord dataset. After 2000-4000 iterations that successfully read batches from the dataset, I get the following error:
...ANSWER
Answered 2022-Feb-14 at 16:16Wrapping the transform_targets_for_output method with a try-except-raise clause and applying tf.data.experimental.ignore_errors to the dataset seems to actually work:
QUESTION
I have a dataset of video_id, user_id and score tensors. I want to filter this to only positive examples with score above a threshold and then remove the score tensor.
...ANSWER
Answered 2022-Feb-07 at 08:56You just need to make sure x['score'] has float values. Here is a working example:
QUESTION
I'm having some issues saving a trained TensorFlow model, where I have a StringLookup layer and I'm required to use TFRecods as input for training. A minimal example to reproduce the issue:
First I define the training data
...ANSWER
Answered 2022-Feb-04 at 17:07Using your data and original vocabulary:
QUESTION
Sorry if there are any mistakes in this question. I come from a PyTorch background but I need to use TFRecordDataset in order to read from TFRecord's. Currently, this looks like the following:
ANSWER
Answered 2022-Jan-26 at 13:31The issue turned out to be using the PyTorch profiler with PyTorch Lightning. The issue was not with Tensorflow.
See relevant issue here
QUESTION
0002cc93b.jpg
29102 12 29346 24...
0007a71bf.jpg
18661 28 18863 82...
000a4bcdd.jpg
131973 1 132228 4...
229501 11 229741 33...
I am trying to create the tfrecords using the above table. I need to get together the rle (Run Length Encoding) feature in the form of rle per class. Eg. the feature in the final tfrecord looks like
ANSWER
Answered 2022-Jan-13 at 20:56You achieve this in 3 simple steps, although it is hard to say what you actually intend to do without further details:
Create and parse data:
QUESTION
I want to use tfrecord to deal with heavy MRI images but I don't know how to. Below is my code, the error and data link. (Sorry if you find the code is a bit long).
About the data:
- 484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
- 484 labels, each has a shape of (240, 240, 155)
First I rearrange my data,
...ANSWER
Answered 2021-Nov-29 at 10:37This error is occurring because you never call close() after writing the example into a file: Here is a working example with random arrays:
QUESTION
I wrote a tfrecord file and fed in my Unet model but got a problem with the input shape. Below is my code.
About the data:
- 484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
- 484 labels, each has a shape of (240, 240, 155)
I used the first 2 examples:
...ANSWER
Answered 2021-Nov-29 at 16:49Your model expects the shape (samples, 240, 240, 155, 4), so try something like this:
QUESTION
I have a dataframe with schema, and want to convert this into tfRecords
...ANSWER
Answered 2022-Jan-12 at 17:29The most probable cause (judging from Maven Central information) is that you're using connector compiled for Scala 2.11 on the Databricks runtime that uses Scala 2.12.
Either you need to use DBR 6.4 for that conversion, or compile connector for Scala 2.12 & use.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install tfrecord
You can use tfrecord like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
