def __init__(self, batch_size, num_threads, device_id):
super(ElementExtractPipeline, self).__init__(batch_size, num_threads, device_id)
self.inputs = ops.ExternalSource()
# Extract first element in each sample
self.element_extract_first = ops.ElementExtract(element_map=[0])
# Extract last element in each sample
self.element_extract_last = ops.ElementExtract(element_map=[F-1])
# Extract both first and last element in each sample to two separate outputs
self.element_extract_first_last = ops.ElementExtract(element_map=[0, F-1])
def __init__(self, batch_size: int, num_threads: int, device_id: int,
sequence_length: int, initial_prefetch_size: int, data: List,
shuffle: bool):
"""
:param batch_size: size of dataset batch
:param num_threads: number of parallel threads
:param device_id: "gpu" or "cpu"
:param sequence_length: number of frames
:param initial_prefetch_size: count of videos readed preliminarily
:param data: input video paths
:param shuffle: suffle samples
"""
super().__init__(batch_size, num_threads, device_id, seed=16)
self.input = ops.VideoReader(device="gpu",
filenames=data,
sequence_length=sequence_length,
shard_id=0,
num_shards=1,
random_shuffle=shuffle,
initial_fill=initial_prefetch_size)
self.extract = ops.ElementExtract(device="gpu",
element_map=list(
range(0, FRAMES_PER_VIDEO)))
self.resize = ops.Resize(device="gpu",
resize_x=config.IMG_SIZE,
resize_y=config.IMG_SIZE)