I am experimenting with video classification using Keras in Cloud ML Engine. My dataset consists in video sequences saved as separate images (eg. seq1_frame1.png, seq1.frame2.png...) which I have uploaded to a GCS bucket.
I use a csv file referencing the start of end frames of different subclips, and a generator which feeds batch of clips to the model. The generator is responsible for loading frames from the bucket, reading them as images, and concatenating them as numpy arrays.
My training is fairly long, and I suspect the generator is my bottleneck due to the numerous reading operations.
In the exemples I found online, people usually save pre-formatted clips as tfrecords files directly to GCS. I feel like this solution isn't ideal for very large datasets as it implies duplicating the data, even more so if we decide to extract overlapping subclips.
Is there something wrong in my approach ? And more importantly, is there a "golden-standard" for using large video datasets for machine learning ?
PS : I explained my setup for reference, but my question is not bound to Keras, generators or Cloud ML.
In this, you are almost always going to be trading time for space. You just have to work out which is more important.
In theory, for every frame, you have height*width*3 bytes. That's assuming 3 colour channels. One possible way you could save space is to use only one channel (probably choose green, or, better still, convert your complete dataset to greyscale). That would reduce your full size video data to one third size. Colour data in video tends to be at a lower resolution than luminance data so it might not affect your training, but it depends on your source files.
As you probably know, .png is a lossless image compression. Every time you load one, the generator will have to decompress first, and then concatenate to the clip. You could save even more space by using a different compression codec, but that would mean every clip would need full decompression and probably add to your time. You're right, the repeated decompression will take time. And saving the video uncompressed will take up quite a lot of space. There are places you could save space, though:
reduce to greyscale (or green scale as above)
temporally subsample frames (do you need EVERY consecutive frame, or could you sample every second one?)
do you use whole frames or just patches? Can you crop or rescale the video sequences?
are you using optical flow? It's pretty processor intensive, consider it as a pre-processing step, too, so you only have to do it once per clip (again this is trading space for time)
Related
I am looking for scaling a PNG file according to an audio provided, a frequency range (20hz-1000hz for example) and a threshold, for a smooth effect.
For example, when there is a kick, scale go to 120% smoothly, I would like to make those audio visualizers such as dubstep, etc... where when kicks comes in, their image are "pumping".
First, is it doable with ffmpeg?
Where to start?
I found showcqt that takes frequencies in input etc., but its output is a video so I don't think I can use it in my case. Any help appreciated.
If you are able to read the PCM values as they are being output, then you might consider using a rolling RMS average in order to get a continuous stream of amplitudes. IDK the best length of the array. Perhaps it should correspond to the number of audio frames that would give you an update for each visual frame? The folks at the DSP site would have the best insights.
If you do a rolling average, computations are not terribly expensive. You'd do the square on the incoming and add that to a ring buffer (circular queue) and drop the outgoing. Only those data points need be added to the rolling average when computing the new rolling average, since the denominator is fixed and known. I found a video that describes the basic RMS math here using Matlab.
It might be necessary to add some smoothing to visualizer that is receiving the volume updates. Also, handing off data from the audio thread should likely employ some form of loose coupling. It would not be good if the thread that is processing the audio was also handling graphics.
I'm a little over my head, but I think this is what is generally done for visualizers.
I am loading in big, raw data files with python. It is a collection of images (video stream) that I want to display on an interface. As of now I am embedding a matplotlib graph by using the imshow() command. However it is very slow.
The fast part is reading the data itself, but splitting it in a numpy array matrix already takes 8 seconds for a 14MB file. We have 50GB files. That would take 8 hours. It's probably not the biggest problem though.
What the real problem is, is displaying the images. Let's say all images of the 14MB file are in RAM memory (I'm assuming python keeps it there. Which is also my problem with python, you don't know what the hell is happening). So right now I am replotting the image every time and then redrawing the canvas, and it seems to be a bottleneck. Is there anyway to reduce this bottleneck?
Images are usually 680*480 (but also variable) of a variable datatype, usually uint8. The interface is a GUI, and there is a slider bar that you can drag to get to a certain frame. An additional feature will be a play button that will go through each frames near real-time. Windows application.
What is suggested (optimal) image size to work with face API. Can't find anything about this.
Looks like images should not be to small but either too large. Probably any recommendation how to prepare them before train model?
Thanks.
This may help from the "Add Face" documentation:
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size is from 1KB to 4MB.
"targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an error. If the provided "targetFace" rectangle is not returned from Face - Detect, there’s no guarantee to detect and add the face successfully.
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions will cause failures.
Adding/deleting faces to/from a same face list are processed sequentially and to/from different face lists are in parallel.
I'm building a network using the Nsynth dataset. It has some 22 Gb of data. Right now I'm loading everything into RAM but this presents some (obvious) problems.
This is an audio dataset and I want to window the signals and produce more examples changing the hop size for example, but because I don't have infinite amounts of RAM there are very little things I can do before I ran out of it (I'm actually only working with a very small subset of the dataset, don't tell google how I live).
Here's some code I'm using right now:
Code:
def generate_audio_input(audio_signal, window_size):
audio_without_silence_at_beginning_and_end = trim_silence(audio_signal, frame_length=window_size)
splited_audio = windower(audio_without_silence_at_beginning_and_end, window_size, hop_size=2048)
return splited_audio
start = time.time()
audios = StrechableNumpyArray()
window_size = 5120
pathToDatasetFolder = 'audio/test'
time_per_loaded = []
time_to_HD = []
for file_name in os.listdir(pathToDatasetFolder):
if file_name.endswith('.wav'):
now = time.time()
audio, sr = librosa.load(pathToDatasetFolder + '/' + file_name, sr=None)
time_to_HD.append(time.time()-now)
output = generate_audio_input(audio, window_size)
audios.append(np.reshape(output, (-1)))
time_per_loaded.append(time.time()-now)
audios = audios.finalize()
audios = np.reshape(audios, (-1, window_size))
np.random.shuffle(audios)
end = time.time()-start
print("wow, that took", end, "seconds... might want to change that to mins :)")
print("On average, it took", np.average(time_per_loaded), "per loaded file")
print("With an standard deviation of", np.std(time_per_loaded))
I'm thinking I could load only the filenames, shuffle those and then yield X loaded results for a more dynamical approach, but in that case I will still have all the different windows for a sound inside those X loaded results, giving me not a very good randomization.
I've also looked into TFRecords but I don't think that would improve anything from what I propose in the last paragraph.
So, to the clear question: Is there a standard way to load/process (audio) data dynamically in tensorflow?
I would appreciate it if the response is tailored to the particular problem I'm addressing of pre-processing my dataset before starting training.
I would also accept it if the answer is pre-process the data and save it into a TFRecord and then load the TFRecord, but I think that's sort of an overkill.
After discussing with some colleges during the last few months, I now think that the standard is indeed to use TFRecords. After making a few and understanding how to work with them I found several advantages and some drawbacks when using them with audio.
Advantages:
They completely all enqueuing issues with minimal strain on RAM.
There are solutions to load examples randomly. How many examples you load on RAM will depend on how frequently you want to go to the HD and how much information you want to load each time you access it.
They are easy to share and the pre-processing is (usually) already incorporated. You can have several processes using them or several people across different continents with a certainty that you are all using the same data. This is not true when working with raw audio and processing it on the fly as different software may apply computations differently (i.e. stft implementations may change soon).
Drawbacks:
They are too static. If you want to change your dataset in any way you need to create a new one. There is no way to modify every or any example. E.g., after a few iterations I decided to discard tensors with low amplitude. I could handle that in the code after loading a batch, but the only sensible way would be to discard the whole batch every time I found an outlier.
Creating them is a cumbersome and slow process. There is no way to start working with a TFRecord until it's complete. Additionally, if you decide to change the size of the tensors or the data type, you're going to have to make extra changes to your code and test them as some errors (e.g. data types) just pass silently.
Large on HD. Because TFRecords have examples that are feed directly into your network, they are not equivalent to raw audio files and you can not erase them. And because some of the examples in the TFRecord are product of data-augmentation techniques, they tend to be larger than the original files. (This last one is probably just a normal consequence of working with big datasets).
All in all, I think even though they are not tailored for audio and they are not very easy to implement at first, they are quite convenient and useful. Which is probably the reason why most people that work with big datasets and whom I've asked this question said they use them.
I am building a prototype of a sound detection app that will ultimately run on a phone (iPhone/Android). It needs to be near real-time to give fast enough response to the user when a particular sound is recognized. I am hoping to use tensorflow to actually build and train the model and then deploy it on mobile device.
What I am unsure about is best way to feed data to tensorflow for inference in this case.
Option 1: Feed only newly acquired samples to the model.
Here the model itself keeps a buffer of previous signal samples, to which new samples are appended and the whole thing get processed.
Something like:
samples = tf.placeholder(tf.int16, shape=(None))
buffer = tf.Variable([], trainable=False, validate_shape=False, dtype=tf.int16)
update_buffer = tf.assign(buffer, tf.concat(0, [buffer, samples]), validate_shape=False)
detection_op = ....process buffer...
session.run([update_buffer, detection_op], feed_dict={samples: [.....]})
This seems to work, but if the samples are pushed to the model 100 times a second, what's happening inside tf.assign (the buffer can grow big enough, and if tf.assign constantly allocates memory this may not work well)?
Option 2: Feed the whole recording to the model
Here the iPhone app keeps the state/recording samples, and feeds the whole recording to the model. The input can get quite large, and re-running the detection op on the whole recording will have to keep recomputing the same values each cycle.
Option 3: Feed a sliding window of data
Here the app keeps the data for the whole recording, but feeds only the latest slice of data to the model. E.g. last 2 sec at 2000 sampling rate == 4000 sample fed fed at the rate of 1/100 sec (each new 20 samples). The model may also need to keep some running totals for the whole recording.
Advise?
I'd need to know a bit more about your application requirements, but for simplicities sake I recommend starting with option #3. The usual way to approach this problem for arbitrary sounds is:
Have some trigger to detect the start of a sound or speech utterance. This can just be sustained audio levels, or something more advanced.
Run a spectrogram over a fixed size window, aligned with the start of the noise.
The rest of the network can just be a standard image detection one (usually cut down in size) to classify the sound.
There are a lot of variations and other possible approaches. For example for speech it's typical to use MFCC as your feature generator, and then run an LSTM to separate out phonemes, but since you mention sound detection I'm guessing you don't need anything this advanced.