Hello! I've found a performance issue in /scripts/imagenet_utils.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:
dataset.batch(batch_size)(here) should be called before dataset.map(_parse_function, num_parallel_calls=num_threads)(here).dataset.batch(batch_size)(here) should be called before dataset.map(_parse_function, num_parallel_calls=num_threads)(here).
Besides, you need to check the function called in map()(e.g., _parse_function called in dataset.map(_parse_function, num_parallel_calls=num_threads)) whether to be affected or not to make the changed code work properly. For example, if _parse_function 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 /scripts/imagenet_utils.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:
dataset.batch(batch_size)(here) should be called beforedataset.map(_parse_function, num_parallel_calls=num_threads)(here).dataset.batch(batch_size)(here) should be called beforedataset.map(_parse_function, num_parallel_calls=num_threads)(here).Besides, you need to check the function called in
map()(e.g.,_parse_functioncalled indataset.map(_parse_function, num_parallel_calls=num_threads)) whether to be affected or not to make the changed code work properly. For example, if_parse_functionneeds 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.