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Loguru logging - #113
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…nto development_robert
…nto development_robert
…nto development_robert
…nto development_robert
Development robert
…nto development_robert
…nto development_robert
Added vibe instances
…nto development_robert
All sliding-window tiles share the same patch_size shape, so cuDNN can autotune the fastest conv algorithms once and reuse them across tiles and images. TF32 accelerates fp32 matmul/conv on Ampere+ GPUs with negligible accuracy impact. Gated behind a new fast_perf flag (default True, CUDA only). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…erence inference_mode disables autograd view/version tracking entirely, so it is slightly faster and uses less memory than no_grad. All tensors here are pure inference outputs (moved to CPU / converted to numpy downstream), so the stricter inference-tensor semantics are safe. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
predict_sliding_window_return_logits runs once per fold; calling torch.cuda.empty_cache() at its start and end returned allocator blocks to the driver right before the next fold reallocated them (slow cudaMalloc + sync). The pool is still cleared once per image after fold averaging, and all OOM-recovery empty_cache() calls are untouched. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
initialize_from_trained_model_folder built loaded_networks by appending the same self.network object once per fold, so every entry ended up holding the LAST fold's weights. predict_logits_from_preprocessed_data then ran that one weight set N times and averaged it, silently collapsing the N-fold ensemble to a single fold while still paying Nx compute. Separately, cache_state_dicts=False left loaded_networks as [] (not None), causing an IndexError in the predict loop. Now loaded_networks is None unless exactly one fold is present (preloaded for zero per-prediction reloads). For >1 fold it stays None and the predict loop swaps weights per fold via load_state_dict, restoring a correct ensemble using a single network's worth of GPU memory. Device-availability warnings now fire once regardless of fold count. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Every run_inference_on_file call previously reloaded the predictor from disk and re-uploaded weights to the GPU. With cache_model=True the loaded predictor is kept in a process-wide cache keyed by model path + folds + device/runtime settings, so a loop over many files reuses the in-memory model. When caching, the end-of-call del/empty_cache is skipped so the CUDA allocator pool stays warm between images. Default is False to preserve current memory semantics; the flag forwards through run_VibeSeg/run_vibeseg/run_nnunet via **kwargs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_run_sub processed one tile per forward pass, leaving the GPU underutilised for small patches. Tiles all share patch_size, so they batch densely: with tile_batch_size>1 they are stacked into a (B, C, *patch) tensor and run in one pass, then scattered back per tile. Mirroring/gaussian/accumulation are unchanged. tile_batch_size=1 (default) takes the original view-based path verbatim, so behaviour and memory are unchanged unless opted in. Threaded through nnUNetPredictor, load_inf_model and run_inference_on_file. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds benchmarks/ with a phase-resolved timing tool for run_inference_on_file (load / preprocess / sliding-window predict / postprocess), using CUDA synchronisation, warmup, repeats and peak-memory tracking. It instruments the pipeline by monkeypatching (no library changes) and signature-filters its kwargs so the same file runs against every commit in the optimisation range. - benchmark_nnunet_inference.py: run/compare subcommands; synthetic or real input. - bench_across_commits.sh: replays one config across baseline+each commit and prints per-commit deltas (isolates the always-on changes; the fold fix shows up as the fold_status column flipping from DUPLICATED to lazy-per-fold/distinct). - bench_flag_sweep.sh: sweeps the opt-in flags at HEAD (cache_model needs >1 call, tile_batch_size, max_folds, step_size). - README.md: usage, which tool measures which commit, and caveats. pyproject: exempt benchmarks/ from docstring rules, mirroring the test dirs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…nto development_robert
Speed up nnUnet inference
…vels) Add loguru dep and a new TPTBox/logger/_loguru_backend.py: each Log_Type maps to a custom TPTBOX_* Loguru level whose color markup reproduces the exact ANSI of type2bcolors (light-cyan->96, bg blue->44, ...). print_to_terminal now emits via a colorized Loguru function-sink (writing to the live sys.stdout, honoring end so end='\r' progress lines are intact). WARNING_THROW still routes to warnings.warn. Public API/classes/verbose semantics unchanged. Verified: 28/28 golden cases render identically (only invisible reset-code differences) and all 15 non-default Log_Types emit the exact expected color code. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…on/retention) Logger now writes via a Loguru file sink instead of raw file.write. Default keeps its own handle behind a Loguru function-sink (flush() works, 'end' honored, ANSI-free, atexit-safe duration line). New rotation=/retention=/enqueue kwargs switch to a Loguru-owned path sink for rotation & retention. Per-instance record filtering keeps multiple Loggers' files isolated. Verified: exact filename scheme + header/duration lines, no ANSI in files, rotation produces multiple files, two-logger isolation, and the duration line is still written at interpreter exit for unclosed loggers. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…r sinks print_error now also emits a structured Loguru record (opt(exception=True), bound to an 'exception' channel the default sinks ignore) so user sinks capture the traceback; the human-readable text output is unchanged. Add install_excepthook() to route uncaught exceptions through Loguru (opt-in). Re-export configure, install_excepthook, and the configured loguru_logger from TPTBox.logger so callers can attach their own sinks (JSON via serialize=True, level filtering on TPTBOX_*). Configure now runs eagerly at import so the take-over logger.remove() only drops Loguru's own default handler and never wipes user sinks added afterwards. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add unit_tests/test_logger.py (10 tests) asserting the backwards-compat contract: per-Log_Type terminal coloring matches type2bcolors, carriage-return end preserved, WARNING_THROW->warnings.warn, verbose gating, ANSI-free + isolated file output, and structured exception capture into user sinks. Document the Loguru backend, TPTBOX_* levels, take-over opt-out, rotation/retention, user sinks, and exception capture in the logger README. Full unit suite: 405 passed / 4 skipped; thread safety verified (3200 concurrent lines, no interleaving). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Summary
Migrates the
TPTBox.loggeremission layer to Loguru while keeping the public API and terminal color coding unchanged. This is a Loguru-backed facade: every public class, method signature,Log_Type,verbosesemantic,sub-logger,
String_Logger,Reflection_Logger, the globallog, theloggingalias,on_*/print_error/log_statistic,.prefix/.default_verbose, andcreate_from_bidsare preserved.Loguru replaces only the two emission primitives (the terminal write and the
Loggerfile write), all isolated behind a newTPTBox/logger/_loguru_backend.py(the only module that imports loguru).Backwards compatibility & color coding
Log_Typemaps to a customTPTBOX_*Loguru level whose color markup reproduces the exact original ANSI (<light-cyan>→\033[96m,<bg blue>→\033[44m,<italic>,<underline>, …).unit_tests/test_logger.py, 10 tests) locks the contract againsttype2bcolors. Verified: 28/28 golden cases render identically (only invisible reset-code differences) and all 15 non-defaultLog_Types emit the exact color code.end="\r"progress lines are preserved (the terminal sink honorsendinstead of Loguru's forced newline),WARNING_THROWstill routes towarnings.warn, andverbose=Falsestill suppresses.Loggers stay isolated.New capabilities (all opt-in, no behavior change by default)
enqueue=Truefor multiprocessing.Logger(..., rotation="20 MB", retention="10 days", enqueue=True).from TPTBox.logger import loguru_logger; loguru_logger.add("run.jsonl", serialize=True, level="TPTBOX_WARNING").print_erroremits a structured record to user sinks; opt-ininstall_excepthook()routes uncaught exceptions through Loguru.Notes for reviewers
TPTBOX_LOGGER_TAKEOVER=0set before importing TPTBox.Configuring eagerly at import (rather than lazily) is deliberate — it ensures user sinks added afterwards are never wiped by the take-over
logger.remove().loguru = "^0.7.2"topyproject.toml.