I'm trying to read binary files that content information of a 3D scene stored as 19 floats followed by a varying number of uint32 values. Since the scene is stored in Run-length encoding (RLE), every binary file has a different size.
Is it possible to read that kind of data using tensorflow?
The equivalent in Matlab looks like this:
filename = 'myFile.bin';
fid = fopen(filename,'r');
vox_origin = fread(fid,3,'float');
camera_poses = fread(fid,16,'float');
labels = fread(fid,'uint32'); % Labels are saved in RLE
fclose(fid);
value = labels(1:2:end);
value_iter = labels(2:2:end);
I don't know any mechanism for this provided by Tensorflow. This is a very basic functionality that you should be able to easily implement or use something like this https://gist.github.com/nvictus/66627b580c13068589957d6ab0919e66. Tensorflow works with numpy arrays.
Related
I am quite new to TensorFlow, and have never worked with TFRecords before.
I have downloaded a dataset of images from online and the download format was TFRecord.
This is the file structure in the downloaded dataset:
1.
2.
E.g. inside "test"
What I want to do is load in the training, validation and testing data into TensorFlow in a similar way to what happens when you load a built-in dataset, e.g. you might load in the MNIST dataset like this, and get arrays containing pixel data and arrays containing the corresponding image labels.
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
However, I have no idea how to do so.
I know that I can use dataset = tf.data.TFRecordDataset(filename) somehow to open the dataset, but would this act on the entire dataset folder, one of the subfolders, or the actual files? If it is the actual files, would it be on the .TFRecord file? How do I use/what do I do with the .PBTXT file which contains a label map?
And even after opening the dataset, how can I extract the data and create the necessary arrays which I can then feed into a TensorFlow model?
It's mostly archaeology, and plus a few tricks.
First, I'd read the README.dataset and README.roboflow files. Can you show us what's in them?
Second, pbtxt are text formatted so we may be able to understand what that file is if you just open it with a text editor. Can you show us what's in that.
The think to remember about a TFRecord file is that it's nothing but a sequence of binary records. tf.data.TFRecordDataset('balls.tfrecord') will give you a dataset that yields those records in order.
Number 3. is the hard part, because here you'll have binary blobs of data, but we don't have any clues yet about how they're encoded.
It's common for TFRecord filed to contian serialized tf.train.Example.
So it would be worth a shot to try and decode it as a tf.train.Example to see if that tells us what's inside.
ref
for record in tf.data.TFRecordDataset('balls.tfrecord'):
break
example = tf.train.Example()
example.ParseFromString(record.numpy())
print(example)
The Example object is just a representation of a dict. If you get something other than en error there look for the dict keys and see if you can make sense out of them.
Then to make a dataset that decodes them you'll want something like:
def decode(record):
return tf.train.parse_example(record, {key:tf.io.RaggedFeature(dtype) for key, dtype in key_dtypes.items()})
ds = ds.map(decode)
I want to apply attention-ocr to detect all digits on number board of cars.
I've read your README.md of attention_ocr on github(https://github.com/tensorflow/models/tree/master/research/attention_ocr), and also the way I should do to use my own image data to train model with the StackOverFlow page.(https://stackoverflow.com/a/44461910/743658)
However, I didn't get any information of how to store annotation or label of the picture, or the format of this problem.
For object detection model, I was able to make my dataset with LabelImg and converting this into csv file, and finally make .tfrecord file.
I want to make .tfrecord file on FSNS dataset format.
Can you give me your advice to go on this training steps?
Please reread the mentioned answer it has a section explaining how to store the annotation. It is stored in the three features image/text, image/class and image/unpadded_class. The image/text field is used for visualization, some models support unpadded sequences and use image/unpadded_class, while the default version relies on the text padded with null characters to have the same length stored in the feature image/class. Here is the excerpt to store the text annotation:
char_ids_padded, char_ids_unpadded = encode_utf8_string(
text, charset, length, null_char_id)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/class': _int64_feature(char_ids_padded),
'image/unpadded_class': _int64_feature(char_ids_unpadded),
'image/text': _bytes_feature(text)
...
}
))
If you have worked with tensorflow object detection, then the apporach should be much easier for you.
You can create the annotation file (in .csv format) using labelImg or any other annotation tool.
However, before converting it into tensorflow format (.tfrecord), you should keep in mind the annotation format. (FSNS format in this case)
The format is : files text xmin ymin xmax ymax
So while annotating dont bother much about the class (as you would have done in object detection !! Some random name should suffice.)
Convert it into .tfrecords.
And finally labelMap is a list of characters which you have annotated.
Hope it helps !
I have some sentences for which I am creating an embedding and it works great for similarity searching unless there are some truly unusual words in the sentence.
In that case, the truly unusual words in fact contain the very most similarity information of any words in the sentence BUT all of that information is lost during embedding due to the fact that the word is apparently not in the vocabulary of the model.
I'd like to get a list of all of the words known by the GUSE embedding model so that I can mask those known words out of my sentence, leaving only the "novel" words.
I can then do an exact word search for those novel words in my target corpus and achieve usability for my similar sentence searching.
e.g. "I love to use Xapian!" gets embedded as "I love to use UNK".
If I just do a keyword search for "Xapian" instead of a semantic similarity search, I'll get much more relevant results than I would using GUSE and vector KNN.
Any ideas on how I can extract the vocabulary known/used by GUSE?
I combine the earlier answer from #Roee Shenberg and the solution provided here to come up with solution, which is applicable for USE v4:
import importlib
loader_impl = importlib.import_module('tensorflow.python.saved_model.loader_impl')
saved_model = loader_impl.parse_saved_model("/tmp/tfhub_modules/063d866c06683311b44b4992fd46003be952409c/")
graph = saved_model.meta_graphs[0].graph_def
fns = [f for f in saved_model.meta_graphs[0].graph_def.library.function if "ptb" in str(f).lower()];
print(len(fns)) # should be 1
nodes_with_sp = [n for n in fns[0].node_def if n.name == "Embeddings_words"]
print(len(nodes_with_sp)) # should be 1
words_tensor = nodes_with_sp[0].attr.get("value").tensor
word_list = [i.decode('utf-8') for i in words_tensor.string_val]
print(len(word_list)) # should be 400004
If you are just curious about the words I upload them here.
I'm assuming you have tensorflow & tensorflow_hub installed, and youhave already downloaded the model.
IMPORTANT: I'm assuming you're looking at https://tfhub.dev/google/universal-sentence-encoder/4! There's no guarantee the object graph looks the same for different versions, it's likely that modifications will be needed.
Find it's location on disk - it's somewhere at /tmp/tfhub_modules unless you set the TFHUB_CACHE_DIR environment variable (Windows/Mac have different locations). The path should contain a file called saved_model.pb, which is the model, serialized using Protocol Buffers.
Unfortunately, the dictionary is serialized inside the model's Protocol Buffers file and not as an external asset, so we'll have to load the model and get the variable from it.
The strategy is to use tensorflow's code to deserialize the file, and then travel down the serialized object tree all the way to the dictionary.
import importlib
MODEL_PATH = 'path/to/model/dir' # e.g. '/tmp/tfhub_modules/063d866c06683311b44b4992fd46003be952409c/'
# Use the tensorflow internal Protobuf loader. A regular import statement will fail.
loader_impl = importlib.import_module('tensorflow.python.saved_model.loader_impl')
saved_model = loader_impl.parse_saved_model(MODEL_PATH)
# reach into the object graph to get the tensor
graph = saved_model.meta_graphs[0].graph_def
function = graph.library.function
node_type, node_value = function[5].node_def
# if you print(node_type) you'll see it's called "text_preprocessor/hash_table"
# as well as get insight into this branch of the object graph we're looking at
words_tensor = node_value.attr.get("value").tensor
word_list = [i.decode('utf-8') for i in words_tensor.string_val]
print(len(word_list)) # -> 400004
Some resources that helped:
A GitHub issue relating to changing the vocabulary
A Tensorflow Google-group thread linked from the issue
Extra Notes
Despite what the GitHub issue may lead you to think, the 400k words here are not the GloVe 400k vocabulary. You can verify this by downloading the GloVe 6B embeddings (file link), extracting glove.6B.50d.txt, and then using the following code to compare the two dictionaries:
with open('/path/to/glove.6B.50d.txt') as f:
glove_vocabulary = set(line.strip().split(maxsplit=1)[0] for line in f)
USE_vocabulary = set(word_list) # from above
print(len(USE_vocabulary - glove_vocabulary)) # -> 281150
Inspecting the different vocabularies is interesting in and of itself, e.g. why does GloVe have an entry for '287.9'?
For encoding categorical data like sex we normally use LabelEncorder() in scikit learn. But If I'm going to use Tensorflow instead of Scikit Learn, what is the equivalent function or methodology for doing such task? I know that we can do one hot encoding easily with tensorflow, but then it will create labels as 10 , 01 instead of 1 , 0.
There is a package in TensorFlow called tf.feature_columns, that contain 4 methods to create categorical columns from your input data:
categorical_column_with_hash_bucket(...): Hash the input value to a fixed number of categories
categorical_column_with_identity(...): If you have numeric input and you want the value itself to be treated as a categorical column
categorical_column_with_vocabulary_list(...): Outputs a category based on a fixed (memory) list of words
categorical_column_with_vocabulary_file(...): Same as _list but reads the vocabulary from file
The package also provides lots more way of getting your input data to the model. For an overview, see this blogpost written by the developers of the package.
I'm currently using python 3.3 in combination with pyaudio and numpy. I took the example from the pyaudio website to play a simple wave file and send that data onto the default sound card.
Now I would like to change the volume of the audio, but when I multiply the array by 0.5, I get a lot of noise and distortion.
Here is a code sample:
while data != '':
decodeddata = numpy.fromstring(data, numpy.int16)
newdata = (decodeddata * 0.5).astype(numpy.int16)
stream.write(newdata.tostring())
data = wf.readframes(CHUNK)
How should I handle multiplication or division on this array without ruining the waveform?
Thanks,
It seemed that the source file's bitrate (24 bit) was not compatible with portaudio. After exporting to a 16 bit pcm file, the multiplication did not result in distortion.
To fix this for different typed files, it is necessary to check the bit depth and rescale correspondingly.