Minimal examples of using NVIDIA/nvvl

minimal_nvvl_tests.py

# Simplest case (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3)
fr = d[0]
print(type(fr))
print(fr.shape)

# Custom processing (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()})
fr = d[0]
print(type(fr))
print(fr['a'].shape)

# Custom labels associated with frames (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, get_label=lambda x,y: (x,y))
fr = d[0]
print(type(fr))
print(fr['default'].shape)
print(fr['labels'])
for x in [50, 121, 31, 50]:
  print(d[x]['labels'])

# Both custom processing and custom labels (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()}, get_label=lambda x,y: (x,y))
fr = d[0]
print(type(fr))
print(fr['a'].shape)
print(fr['labels'])
for x in [50, 121, 31, 50]:
  print(d[x]['labels'])

# Simplest Dataloader use
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3)
lo = nvvl.VideoLoader(d, batch_size=3)
it = lo.__iter__()
fr = next(it)
print(type(fr))
print(fr['default'].shape)

# Sequential Dataloader with custom processing
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()})
lo = nvvl.VideoLoader(d, batch_size=3)
it = lo.__iter__()
fr = next(it)
print(type(fr))
print(fr['a'].shape)

# Sequential Dataloader with default processing and custom labels
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, get_label=lambda x,y: (x,y))
lo = nvvl.VideoLoader(d, batch_size=3)
it = lo.__iter__()
fr = next(it)
print(type(fr))
print(fr['default'].shape)

# Sequential Dataloader with custom processing and custom labels
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()}, get_label=lambda x,y: (x,y))
lo = nvvl.VideoLoader(d, batch_size=3)
it = lo.__iter__()
fr = next(it)
print(type(fr))
print(fr['a'].shape)
print(fr['labels'])

# Shuffled Dataloader with custom processing and custom labels
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()}, get_label=lambda x,y: (x,y))
lo = nvvl.VideoLoader(d, batch_size=3, shuffle=True)
it = lo.__iter__()
fr = next(it)
print(type(fr))
print(fr['a'].shape)
print(fr['labels'])