Add missing lambda to transforms in tutorial #49
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Summary:
## Motivation and Context
Adds the ability to set the stride < window size for UniformClipSampler. Why?
- Feature extraction on imagenet based models for videos, e.g. every 5 frames with a single frame retrieved
## How Has This Been Tested
- Added some unit tests
- Run existing unit tests
## Other comments
This was a bit difficult to implement due to the way clip samplers are designed. Personally I think this can be re-designed such that the implementation is much simpler. As an example, the implementation of UniformClipSampler could be ~20 lines of code compared to the current implementation:
```
def _num_clips(...) -> int:
num_frames = round(duration_sec * fps)
N = num_frames - window_size_frames
if N < 0:
return 1
result = int(N / stride_frames + 1)
pad = backpad_last and N % stride_frames != 0
return result + pad
start_end_times = [
(
(i * s_prime) / fps,
((i * s_prime + window_size) / fps)
)
for i in range(num_clips(...))
]
if expected_start_end_times[-1][1] - video_length > 1e-6:
expected_start_end_times[-1] = (video_length - window_size / fps, video_length)
```
X-link: fairinternal/pytorchvideo#49
Reviewed By: lyttonhao
Differential Revision: D34811417
Pulled By: miguelmartin75
fbshipit-source-id: b9d93d5446d16008ede20e61a6314a89e6b1364e
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Motivation and Context
Closes #32 by adding lambda in the tutorial. Code changes to be made in #41 - this is just a quickfix.
How Has This Been Tested
Types of changes
Checklist