|
prev_filters = int(np.prod(inshape) / np.prod(outshape[1:])) |
In the line:
prev_filters = int(np.prod(inshape) / np.prod(outshape[1:]))
a more efficient alternative is:
prev_filters = int(np.multiply.reduce(inshape) / np.multiply.reduce(outshape[1:]))
np.prod() is a convenience wrapper around np.multiply.reduce() and includes extra logic such as input validation, type conversion, and parameter parsing. In performance-sensitive computations like shape transformations, using np.multiply.reduce() avoids overhead and results in a faster execution path.
This change is small but improves performance and aligns with best practices for low-level array operations.
Fewshot_Detection/cfg.py
Line 393 in ddfd3fd
In the line:
prev_filters = int(np.prod(inshape) / np.prod(outshape[1:]))a more efficient alternative is:
prev_filters = int(np.multiply.reduce(inshape) / np.multiply.reduce(outshape[1:]))np.prod() is a convenience wrapper around np.multiply.reduce() and includes extra logic such as input validation, type conversion, and parameter parsing. In performance-sensitive computations like shape transformations, using np.multiply.reduce() avoids overhead and results in a faster execution path.
This change is small but improves performance and aligns with best practices for low-level array operations.