I'm using TensorFlow Alpha 2.0.
I have TFRecords files I'm reading from, each one holding a short video clip with each frame encoded as jpeg byte string to save space:
{
'numframes': tf.io.FixedLenFeature([], tf.int64),
'frames': tf.io.VarLenFeature(tf.string)
}
I have a map step in my tf.data.Dataset pipeline that successfully parses each example:
def parse_tfrecord(p):
return tf.io.parse_single_example(p, example_schema)
My next step is to read out the number of frames from numframes and run the tf.io.decode_jpeg function on each frame in frames.values[i] with i being from range(numframes):
def parse_jpegs(p):
numframes = p['numframes']
return tf.map_fn(tf.io.decode_jpeg, [p['frames'].values[i] for i in range(numframes)])
My dataset pipeline for completeness:
def dataset():
dataset = tf.data.Dataset.list_files("*.tfrecord")
dataset = tf.data.TFRecordDataset(dataset)
dataset = dataset.shuffle(1000).repeat()
dataset = dataset.map(parse_tfrecord)
dataset = dataset.map(parse_jpegs)
return dataset
If I exclude the dataset.map(parse_jpegs) line it all works alright, showing me something like {'frames': , 'numframes': }
(Note that the numframes tensor includes a numpy value of 25. I can get that outside my dataset pipeline with the tensor.numpy() method)
Within that map function though, I can't call .numpy() to get the value out of the tensor, and when printing the tensor itself it hasn't been evaluated or something because there is no value shown yet.
What is the best way to parse all these frames within the dataset pipeline?
EDIT: Error message I'm getting is TypeError: 'Tensor' object cannot be interpreted as an integer in parse_jpegs when trying to get numframes. This makes sense to me why a tensor can't be interpreted as an int, but how can I get the value from that tensor to use to set the range?
The problem I'm running into comes down to the fact that each "frames" object has a different number of frames. If I can apply tf.io.decode_jpeg to each frame in that list without needing to record number of frames separately I would be fine with that, but I have "numframes" here so I know how many frames need to be decoded in my "frames" list.
EDIT: I'll heave the question up for anyone else who might find it helpful, but I ended up just returning the raw bytestrings and doing the decode_jpeg in a separate generator function outside the dataset API. It was much easier that way, even if it might be slower.
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