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MixMatch Domain Adaptation: Prize-winning solution for both tracks of VisDA 2019 challenge

License: Other

Python 100.00%
domain-adaptation visda mixmatch efficient-net tensorflow

visda2019's Issues

Question of function run_balanced

visda2019/models/common.py

Lines 68 to 81 in cb3352d

def run_balanced(models, tensors):
n_tensors = len(tensors)
splitted_tensors = tuple(tf.split(tensors[i], n_tensors) for i in range(n_tensors))
results = []
for i in range(n_tensors):
combined_tensors = tf.concat(tuple(splitted_tensors[j][i] for j in range(n_tensors)), axis=0)
for model in models:
combined_tensors = model(combined_tensors, training=True)
results.append(combined_tensors)
splitted_results = tuple(tf.split(results[i], n_tensors) for i in range(n_tensors))
combined_results = []
for i in range(n_tensors):
combined_results.append(tf.concat(tuple(splitted_results[j][i] for j in range(n_tensors)), axis=0))
return tuple(combined_results)

Hi, thanks for the wonderful work! After reading the code, I have a question about the function run_balanced.

It seems to find the transposition of tensors first, and then convert it to the original order after feeding it to the models. I am confused about its function and usage...

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