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Hi, based on the following lines, it seems gradient accumulation is not properly implemented:
HIPT/2-Weakly-Supervised-Subtyping/utils/core_utils.py
Lines 285 to 290 in a9b5bb8
A proper implementation should look like the following:
loss = loss / gc loss.backward() if (batch_idx + 1) % gc == 0: optimizer.step() optimizer.zero_grad()
The text was updated successfully, but these errors were encountered:
Hi, based on the following lines, it seems gradient accumulation is not properly implemented: HIPT/2-Weakly-Supervised-Subtyping/utils/core_utils.py Lines 285 to 290 in a9b5bb8 loss = loss / gc loss.backward() # step optimizer.step() optimizer.zero_grad() A proper implementation should look like the following: loss = loss / gc loss.backward() if (batch_idx + 1) % gc == 0: optimizer.step() optimizer.zero_grad()
Hi! I'm also working on reproducing this HIPT paper. Would you be interested in some discussion?
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sure, happy to chat. I’ve made my own version of the code here: https://github.com/clemsgrs/hipt
you can contact me at: clement (dot) grisi (at) radboudumc (dot) nl
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Hi, based on the following lines, it seems gradient accumulation is not properly implemented:
HIPT/2-Weakly-Supervised-Subtyping/utils/core_utils.py
Lines 285 to 290 in a9b5bb8
A proper implementation should look like the following:
The text was updated successfully, but these errors were encountered: