Hello! I've found a performance issue in /NNDenseInput.py: .batch(batch_size=config.MODEL.batch_size,drop_remainder=config.MODEL.drop_remainder)(here) should be called before .map(map_func=_parse_single_example,num_parallel_calls=parallel)(here), which could make your program more efficient.
Here is the tensorflow document to support it.
Besides, you need to check the function _parse_single_example called in .map(map_func=_parse_single_example,num_parallel_calls=parallel) whether to be affected or not to make the changed code work properly. For example, if _parse_single_example needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z) after fix.
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
Hello! I've found a performance issue in /NNDenseInput.py:
.batch(batch_size=config.MODEL.batch_size,drop_remainder=config.MODEL.drop_remainder)(here) should be called before.map(map_func=_parse_single_example,num_parallel_calls=parallel)(here), which could make your program more efficient.Here is the tensorflow document to support it.
Besides, you need to check the function
_parse_single_examplecalled in.map(map_func=_parse_single_example,num_parallel_calls=parallel)whether to be affected or not to make the changed code work properly. For example, if_parse_single_exampleneeds data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z) after fix.Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.