Comments (2)
Thank you for open-sourcing the code. This article has been very insightful and inspiring to me. However, I have some questions while reviewing the code.
Q1:This function currently appears to only have the Lc loss from the paper and does not include the loss from the Lr component.
Q2:In the get_loss_img2text function, the loss and extra_loss within the if branch do not correspond to those in the else branch.
Hi, Thank you for your interest!
First, I apologize for the raw code presented in the repo, promise I will reformat and add more docstrings.
For the first question, in our real implementation, we seperately train the two branches (Each with 4 GPUs). The get_loss_img2text_image function is the image-only contrastive branch and the get_loss_img2text is the textual alignment branch.
As for the second question, we only train the model using 4 cards thus you can always refer to the if branch. The else branch is only used for debug which can be ignored.
from keds.
Thank you for open-sourcing the code. This article has been very insightful and inspiring to me. However, I have some questions while reviewing the code.
Q1:This function currently appears to only have the Lc loss from the paper and does not include the loss from the Lr component.
Q2:In the get_loss_img2text function, the loss and extra_loss within the if branch do not correspond to those in the else branch.
Q1:
Q2:Hi, Thank you for your interest!
First, I apologize for the raw code presented in the repo, promise I will reformat and add more docstrings.
For the first question, in our real implementation, we seperately train the two branches (Each with 4 GPUs). The get_loss_img2text_image function is the image-only contrastive branch and the get_loss_img2text is the textual alignment branch.
As for the second question, we only train the model using 4 cards thus you can always refer to the if branch. The else branch is only used for debug which can be ignored.
I understand now. Thank you for your response. ^_^
from keds.
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