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Hello! I've found a performance issue in /image/data.py: dataset = dataset.batch(per_core_bsz, drop_remainder=True)(here) should be called before dataset = dataset.map(parser, num_parallel_calls=32)(here), which would make your program more efficient.
Besides, you need to check the function parser(here) called in dataset.map() whether to be affected or not to make the changed code work properly. For example, if parser needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z).
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
The text was updated successfully, but these errors were encountered:
Hello! I've found a performance issue in /image/data.py:
dataset = dataset.batch(per_core_bsz, drop_remainder=True)
(here) should be called beforedataset = dataset.map(parser, num_parallel_calls=32)
(here), which would make your program more efficient.Here is the tensorflow document to support it.
Besides, you need to check the function
parser
(here) called indataset.map()
whether to be affected or not to make the changed code work properly. For example, ifparser
needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z).Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
The text was updated successfully, but these errors were encountered: