Memory

pytme is aware of the memory required for template matching operations. This enables us to map template matching problems to the available hardware and compute a splitting strategy ahead of time.

Memory usage helpers

estimate_memory_usage(shape1, shape2, ...[, ...])

Estimate the memory usage for a given template matching run.

Abstract Base

MatchingMemoryUsage(fast_shape, ft_shape, ...)

Strategy class for estimating memory requirements.

Memory usage classes

MemoryProfile(fast_shape, ft_shape, ...)	Memory estimator for methods with uniform array requirements.
CCMemoryUsage(fast_shape, ft_shape, ...)	tme.matching_scores.cc_setup() memory estimator.
CORRMemoryUsage(fast_shape, ft_shape, ...)	tme.matching_scores.corr_setup() memory estimator.
FLCMemoryUsage(fast_shape, ft_shape, ...)	tme.matching_scores.flc_setup() memory estimator.
MCCMemoryUsage(fast_shape, ft_shape, ...)	tme.matching_scores.mcc_setup() memory estimator.
MaxScoreOverRotationsMemoryUsage(fast_shape, ...)	tme.analyzer.MaxScoreOverRotations memory estimator.
MaxScoreOverRotationsConstrainedMemoryUsage(...)	tme.analyzer.MaxScoreOverRotationsConstrained memory estimator.
PeakCallerMaximumFilterMemoryUsage(...)	tme.analyzer.peaks.PeakCallerMaximumFilter memory estimator.
CupyBackendMemoryUsage(fast_shape, ft_shape, ...)	tme.backends.CupyBackend memory estimator.