Hello! I've found a performance issue in input_fn.py: batch() should be called before map(), which could make your program more efficient. Here is the tensorflow document to support it.
Detailed description is listed below:
tensorflow/vision/model/input_fn.py: .batch(params.batch_size)(here) should be called before .map(parse_fn, num_parallel_calls=params.num_parallel_calls)(here).
tensorflow/vision/model/input_fn.py: .batch(params.batch_size)(here) should be called before .map(parse_fn)(here).
Besides, you need to check the function called in map()(e.g., parse_fn called in .map(parse_fn)) whether to be affected or not to make the changed code work properly. For example, if parse_fn 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.
Hello! I've found a performance issue in input_fn.py:
batch()
should be called beforemap()
, which could make your program more efficient. Here is the tensorflow document to support it.Detailed description is listed below:
.batch(params.batch_size)
(here) should be called before.map(parse_fn, num_parallel_calls=params.num_parallel_calls)
(here)..batch(params.batch_size)
(here) should be called before.map(parse_fn)
(here).Besides, you need to check the function called in
map()
(e.g.,parse_fn
called in.map(parse_fn)
) whether to be affected or not to make the changed code work properly. For example, ifparse_fn
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.