This code is implemented based on Soft DTW for PyTorch in CUDA depending on PyTorch and Numba. It includes three choice:
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update2_down: down or along the main diagonal.
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update2_right: right or along the main diagonal.
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update3: down or right or along the main diagonal.
You can also run the included profiler/test (tested with Python v3.6), and see the speedups you'd get:
git clone https://github.com/wangzehui20/OTAM-Video-via-Temporal-Alignment
cd OTAM-Video-via-Temporal-Alignment
python softerdtw_padquery_update2_right.py
A sample code is already provided in the script. Here's a quick example:
from softerdtw_padquery_update2_right import SoftDTW
# Create the sequences
batch_size, len_x, len_y, dims = 8, 15, 12, 5
x = torch.rand((batch_size, len_x, dims), requires_grad=True)
y = torch.rand((batch_size, len_y, dims))
# Create the "criterion" object
sdtw = SoftDTW(use_cuda=True, gamma=0.1)
# Compute the loss value
loss = sdtw(x, y) # Just like any torch.nn.xyzLoss()
# Aggregate and call backward()
loss.mean().backward()
you can learn about algorithm in Few-Shot Video Classification via Temporal Alignment.
If you want to see alignment path between two features, you can run ...path.py in save path file.
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