From f46e88c8ce6a4e74738bea023f43d5e5df714cde Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Thu, 18 Sep 2025 10:20:31 -0400 Subject: [PATCH 01/44] feat: add base and runner classes for generic post-processing framework --- ami/ml/post_processing/base.py | 24 ++++++++++++++++++++++++ ami/ml/post_processing/runner.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 52 insertions(+) create mode 100644 ami/ml/post_processing/base.py create mode 100644 ami/ml/post_processing/runner.py diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py new file mode 100644 index 000000000..759438ab0 --- /dev/null +++ b/ami/ml/post_processing/base.py @@ -0,0 +1,24 @@ +import logging +from typing import Any + +logger = logging.getLogger(__name__) + +POST_PROCESSING_REGISTRY = {} + + +def register_step(cls): + """Decorator to register a post-processing step.""" + POST_PROCESSING_REGISTRY[cls.name] = cls() + return cls + + +class PostProcessingStep: + """Base interface for a post-processing step.""" + + name: str = "base_step" + description: str = "Base step (does nothing)" + default_enabled: bool = False + + def apply(self, pipeline_input: Any, pipeline_output: Any) -> tuple[Any, Any]: + """Process and return modified input/output.""" + raise NotImplementedError diff --git a/ami/ml/post_processing/runner.py b/ami/ml/post_processing/runner.py new file mode 100644 index 000000000..3a660f6da --- /dev/null +++ b/ami/ml/post_processing/runner.py @@ -0,0 +1,28 @@ +import logging +from typing import Any + +from .base import POST_PROCESSING_REGISTRY + +logger = logging.getLogger(__name__) + + +def run_postprocessing( + pipeline_input: Any, + pipeline_output: Any, + enabled_steps: list[str] | None = None, +) -> tuple[Any, Any]: + """ + Run all enabled post-processing steps on pipeline results. + """ + steps = enabled_steps or [name for name, step in POST_PROCESSING_REGISTRY.items() if step.default_enabled] + + logger.info(f"Running post-processing steps: {steps}") + + for name in steps: + step = POST_PROCESSING_REGISTRY.get(name) + if not step: + logger.warning(f"Post-processing step '{name}' not found, skipping") + continue + pipeline_input, pipeline_output = step.apply(pipeline_input, pipeline_output) + + return pipeline_input, pipeline_output From d86ea4de7b6e7282858231ff99380aa1f5e7b8d5 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:09:08 -0400 Subject: [PATCH 02/44] feat: add post-processing framework base post-processing task class --- ami/ml/post_processing/__init__.py | 1 + ami/ml/post_processing/base.py | 82 +++++++++++++++++++++++++----- 2 files changed, 69 insertions(+), 14 deletions(-) create mode 100644 ami/ml/post_processing/__init__.py diff --git a/ami/ml/post_processing/__init__.py b/ami/ml/post_processing/__init__.py new file mode 100644 index 000000000..3517ed47c --- /dev/null +++ b/ami/ml/post_processing/__init__.py @@ -0,0 +1 @@ +from . import small_size_filter # noqa: F401 diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py index 759438ab0..055f925ce 100644 --- a/ami/ml/post_processing/base.py +++ b/ami/ml/post_processing/base.py @@ -1,24 +1,78 @@ +# ami/ml/post_processing/base.py + +import abc import logging from typing import Any -logger = logging.getLogger(__name__) +from ami.jobs.models import Job +from ami.ml.models import Algorithm, AlgorithmTaskType + +# Registry of available post-processing tasks +POSTPROCESSING_TASKS: dict[str, type["BasePostProcessingTask"]] = {} + + +def register_postprocessing_task(task_cls: type["BasePostProcessingTask"]): + """ + Decorator to register a post-processing task in the global registry. + Each task must define a unique `key`. + Ensures an Algorithm entry exists for this task. + """ + if not hasattr(task_cls, "key") or not task_cls.key: + raise ValueError(f"Task {task_cls.__name__} missing required 'key' attribute") + + # Register the task + POSTPROCESSING_TASKS[task_cls.key] = task_cls + + # Ensure Algorithm object exists for this task + algorithm, _ = Algorithm.objects.get_or_create( + name=task_cls.__name__, + defaults={ + "description": f"Post-processing task: {task_cls.key}", + "task_type": AlgorithmTaskType.POST_PROCESSING.value, + }, + ) + + # Attach the Algorithm object to the task class + task_cls.algorithm = algorithm + + return task_cls + -POST_PROCESSING_REGISTRY = {} +def get_postprocessing_task(name: str) -> type["BasePostProcessingTask"] | None: + """ + Get a task class by its registry key. + Returns None if not found. + """ + return POSTPROCESSING_TASKS.get(name) -def register_step(cls): - """Decorator to register a post-processing step.""" - POST_PROCESSING_REGISTRY[cls.name] = cls() - return cls +class BasePostProcessingTask(abc.ABC): + """ + Abstract base class for all post-processing tasks. + """ + # Each task must override these + key: str = "" + name: str = "" -class PostProcessingStep: - """Base interface for a post-processing step.""" + def __init__(self, **config: Any): + """ + Initialize task with configuration parameters. + """ + self.config: dict[str, Any] = config + self.logger = logging.getLogger(f"ami.post_processing.{self.key}") - name: str = "base_step" - description: str = "Base step (does nothing)" - default_enabled: bool = False + @abc.abstractmethod + def run(self, job: Job) -> None: + """ + Run the task logic. + Must be implemented by subclasses. + The job parameter provides context (project, logs, etc.). + """ + raise NotImplementedError("Subclasses must implement run()") - def apply(self, pipeline_input: Any, pipeline_output: Any) -> tuple[Any, Any]: - """Process and return modified input/output.""" - raise NotImplementedError + def log_config(self, job: Job): + """ + Helper to log the task configuration at start. + """ + job.logger.info(f"Running task {self.name} ({self.key}) with config: {self.config}") From 2c0f78fbd87b4984342ce88b45255261b1868fb8 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:10:52 -0400 Subject: [PATCH 03/44] feat: add small size filter post-processing task class --- ami/ml/post_processing/small_size_filter.py | 92 +++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 ami/ml/post_processing/small_size_filter.py diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py new file mode 100644 index 000000000..3487bab42 --- /dev/null +++ b/ami/ml/post_processing/small_size_filter.py @@ -0,0 +1,92 @@ +from django.db import transaction +from django.utils import timezone + +from ami.jobs.models import Job +from ami.main.models import Detection, SourceImageCollection, Taxon, TaxonRank +from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task + + +@register_postprocessing_task +class SmallSizeFilter(BasePostProcessingTask): + key = "small_size_filter" + name = "Small Size Filter" + + def run(self, job: "Job") -> None: + threshold = self.config.get("size_threshold", 0.01) + collection_id = self.config.get("source_image_collection_id") + # Get or create the "Not identifiable" taxon + + not_identifiable_taxon, _ = Taxon.objects.get_or_create( + name="Not identifiable", + defaults={ + "rank": TaxonRank.UNKNOWN, + "notes": "Auto-generated taxon for small size filter", + }, + ) + job.logger.info(f"=== Starting {self.name} ===") + + if not collection_id: + msg = "Missing required config param: source_image_collection_id" + job.logger.error(msg) + raise ValueError(msg) + + try: + collection = SourceImageCollection.objects.get(pk=collection_id) + job.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project_id})") + except SourceImageCollection.DoesNotExist: + msg = f"SourceImageCollection {collection_id} not found" + job.logger.error(msg) + raise ValueError(msg) + + detections = Detection.objects.filter(source_image__collections=collection) + total = detections.count() + job.logger.info(f"Found {total} detections in collection {collection_id}") + + modified = 0 + + for det in detections.iterator(): + bbox = det.get_bbox() + if not bbox: + job.logger.debug(f"Detection {det.pk}: no bbox, skipping") + continue + + img_w, img_h = det.source_image.width, det.source_image.height + if not img_w or not img_h: + job.logger.debug(f"Detection {det.pk}: missing source image dims, skipping") + continue + + det_area = det.width() * det.height() + img_area = img_w * img_h + rel_area = det_area / img_area if img_area else 0 + + job.logger.debug( + f"Detection {det.pk}: area={det_area}, rel_area={rel_area:.4f}, " f"threshold={threshold:.4f}" + ) + + if rel_area < threshold: + with transaction.atomic(): + # Mark existing classifications as non-terminal + det.classifications.update(terminal=False) + + # Create the new "Not identifiable" classification + det.classifications.create( + detection=det, + taxon=not_identifiable_taxon, + score=1.0, + terminal=True, + timestamp=timezone.now(), + algorithm=self.algorithm, + ) + + modified += 1 + job.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") + + job.logger.info(f"=== Completed {self.name}: {modified}/{total} detections modified ===") + + job.result = { + "detections_total": total, + "detections_modified": modified, + "threshold": threshold, + "collection_id": collection_id, + } + job.save(update_fields=["result"]) From ffba70964ec9e7b6f328c31a2d8f5f173ea210e7 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:12:05 -0400 Subject: [PATCH 04/44] feat: add post processing job type --- ami/jobs/models.py | 40 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/ami/jobs/models.py b/ami/jobs/models.py index f7b85283b..39e7a0ff3 100644 --- a/ami/jobs/models.py +++ b/ami/jobs/models.py @@ -645,6 +645,37 @@ def run(cls, job: "Job"): job.update_status(JobState.SUCCESS, save=True) +class PostProcessingJob(JobType): + name = "Post Processing" + key = "post_processing" + + @classmethod + def run(cls, job: "Job"): + import ami.ml.post_processing # noqa F401 + from ami.ml.post_processing.base import get_postprocessing_task + + job.progress.add_stage(cls.name, key=cls.key) + job.update_status(JobState.STARTED) + job.started_at = datetime.datetime.now() + job.save() + + params = job.params or {} + task_key: str = params.get("task", "") + config = params.get("config", {}) + job.logger.info(f"Post-processing task: {task_key} with params: {job.params}") + + task_cls = get_postprocessing_task(task_key) + if not task_cls: + raise ValueError(f"Unknown post-processing task '{task_key}'") + + task = task_cls(**config) + task.run(job) + job.progress.update_stage(cls.key, status=JobState.SUCCESS, progress=1) + job.finished_at = datetime.datetime.now() + job.update_status(JobState.SUCCESS) + job.save() + + class UnknownJobType(JobType): name = "Unknown" key = "unknown" @@ -654,7 +685,14 @@ def run(cls, job: "Job"): raise ValueError(f"Unknown job type '{job.job_type()}'") -VALID_JOB_TYPES = [MLJob, SourceImageCollectionPopulateJob, DataStorageSyncJob, UnknownJobType, DataExportJob] +VALID_JOB_TYPES = [ + MLJob, + SourceImageCollectionPopulateJob, + DataStorageSyncJob, + UnknownJobType, + DataExportJob, + PostProcessingJob, +] def get_job_type_by_key(key: str) -> type[JobType] | None: From 63cd84b456aa4e819ceb32da495d9b3c489cc08d Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:12:33 -0400 Subject: [PATCH 05/44] feat: trigger small size filter post processing task from admin page --- ami/main/admin.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index dd2cea5c5..a3f4d6aae 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -10,6 +10,7 @@ import ami.utils from ami import tasks +from ami.jobs.models import Job from ami.ml.models.project_pipeline_config import ProjectPipelineConfig from ami.ml.tasks import remove_duplicate_classifications @@ -619,7 +620,28 @@ def populate_collection_async(self, request: HttpRequest, queryset: QuerySet[Sou f"Populating {len(queued_tasks)} collection(s) background tasks: {queued_tasks}.", ) - actions = [populate_collection, populate_collection_async] + @admin.action(description="Run Small Size Filter post-processing task (async)") + def run_small_size_filter(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: + jobs = [] + for collection in queryset: + job = Job.objects.create( + name=f"Post-processing: SmallSizeFilter on Collection {collection.pk}", + project=collection.project, + job_type_key="post_processing", + params={ + "task": "small_size_filter", + "config": { + "size_threshold": 0.01, # default threshold + "source_image_collection_id": collection.pk, + }, + }, + ) + job.enqueue() + jobs.append(job.pk) + + self.message_user(request, f"Queued Small Size Filter for {queryset.count()} collection(s). Jobs: {jobs}") + + actions = [populate_collection, populate_collection_async, run_small_size_filter] # Hide images many-to-many field from form. This would list all source images in the database. exclude = ("images",) From cab62bf02e88e2abd13edd27aa6af7e428601d2f Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:13:22 -0400 Subject: [PATCH 06/44] feat: add a new algorithm task type for post-processing --- ami/ml/models/algorithm.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ami/ml/models/algorithm.py b/ami/ml/models/algorithm.py index 48b2e2336..0e9df4609 100644 --- a/ami/ml/models/algorithm.py +++ b/ami/ml/models/algorithm.py @@ -176,6 +176,7 @@ class AlgorithmTaskType(str, enum.Enum): DEPTH_ESTIMATION = "depth_estimation" POSE_ESTIMATION = "pose_estimation" SIZE_ESTIMATION = "size_estimation" + POST_PROCESSING = "post_processing" OTHER = "other" UNKNOWN = "unknown" From 6d0e284d0ceb952945c2c50767a3737bcd06603c Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:15:57 -0400 Subject: [PATCH 07/44] chore: deleted runner.py --- ami/ml/post_processing/runner.py | 28 ---------------------------- 1 file changed, 28 deletions(-) delete mode 100644 ami/ml/post_processing/runner.py diff --git a/ami/ml/post_processing/runner.py b/ami/ml/post_processing/runner.py deleted file mode 100644 index 3a660f6da..000000000 --- a/ami/ml/post_processing/runner.py +++ /dev/null @@ -1,28 +0,0 @@ -import logging -from typing import Any - -from .base import POST_PROCESSING_REGISTRY - -logger = logging.getLogger(__name__) - - -def run_postprocessing( - pipeline_input: Any, - pipeline_output: Any, - enabled_steps: list[str] | None = None, -) -> tuple[Any, Any]: - """ - Run all enabled post-processing steps on pipeline results. - """ - steps = enabled_steps or [name for name, step in POST_PROCESSING_REGISTRY.items() if step.default_enabled] - - logger.info(f"Running post-processing steps: {steps}") - - for name in steps: - step = POST_PROCESSING_REGISTRY.get(name) - if not step: - logger.warning(f"Post-processing step '{name}' not found, skipping") - continue - pipeline_input, pipeline_output = step.apply(pipeline_input, pipeline_output) - - return pipeline_input, pipeline_output From 4cfe2d8dcc21447470adf38c18c3ebc567f1d9e6 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 13:19:12 -0400 Subject: [PATCH 08/44] feat: add migration for creating a new job type --- .../migrations/0018_alter_job_job_type_key.py | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 ami/jobs/migrations/0018_alter_job_job_type_key.py diff --git a/ami/jobs/migrations/0018_alter_job_job_type_key.py b/ami/jobs/migrations/0018_alter_job_job_type_key.py new file mode 100644 index 000000000..e51a84b08 --- /dev/null +++ b/ami/jobs/migrations/0018_alter_job_job_type_key.py @@ -0,0 +1,29 @@ +# Generated by Django 4.2.10 on 2025-09-30 12:25 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + dependencies = [ + ("jobs", "0017_alter_job_logs_alter_job_progress"), + ] + + operations = [ + migrations.AlterField( + model_name="job", + name="job_type_key", + field=models.CharField( + choices=[ + ("ml", "ML pipeline"), + ("populate_captures_collection", "Populate captures collection"), + ("data_storage_sync", "Data storage sync"), + ("unknown", "Unknown"), + ("data_export", "Data Export"), + ("post_processing", "Post Processing"), + ], + default="unknown", + max_length=255, + verbose_name="Job Type", + ), + ), + ] From b42e06929e493e0e9c7e42df74f476fe9216a4f8 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 30 Sep 2025 14:35:31 -0400 Subject: [PATCH 09/44] fix: fix an import error with the AlgorithmTaskType --- ami/ml/post_processing/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py index 055f925ce..baa1c452e 100644 --- a/ami/ml/post_processing/base.py +++ b/ami/ml/post_processing/base.py @@ -5,7 +5,8 @@ from typing import Any from ami.jobs.models import Job -from ami.ml.models import Algorithm, AlgorithmTaskType +from ami.ml.models import Algorithm +from ami.ml.models.algorithm import AlgorithmTaskType # Registry of available post-processing tasks POSTPROCESSING_TASKS: dict[str, type["BasePostProcessingTask"]] = {} From cb7c83a465c28f5fc8eb34dee6c66f8265851780 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Thu, 2 Oct 2025 10:54:09 -0400 Subject: [PATCH 10/44] feat: update identification history of occurrences in SmallSizeFilter --- ami/ml/post_processing/small_size_filter.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index 3487bab42..784647dd8 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -2,7 +2,7 @@ from django.utils import timezone from ami.jobs.models import Job -from ami.main.models import Detection, SourceImageCollection, Taxon, TaxonRank +from ami.main.models import Detection, Identification, SourceImageCollection, Taxon, TaxonRank from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task @@ -77,7 +77,14 @@ def run(self, job: "Job") -> None: timestamp=timezone.now(), algorithm=self.algorithm, ) - + # Also create/update Identification for the linked occurrence + if det.occurrence: + Identification.objects.create( + occurrence=det.occurrence, + taxon=not_identifiable_taxon, + user=None, # since this comes from a post-processing algorithm not a human + comment=f"Auto-set by {self.name} Filter post-processing task", + ) modified += 1 job.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") From 10103db3318282ea632e2c16fa34322ee220db5d Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Mon, 6 Oct 2025 10:59:57 -0400 Subject: [PATCH 11/44] feat: add rank rollup --- ami/ml/post_processing/rank_rollup.py | 127 ++++++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 ami/ml/post_processing/rank_rollup.py diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py new file mode 100644 index 000000000..01a315d3e --- /dev/null +++ b/ami/ml/post_processing/rank_rollup.py @@ -0,0 +1,127 @@ +import logging +from collections import defaultdict + +from django.db import transaction +from django.utils import timezone +from main.models import Classification, Identification, Taxon + +from ami.jobs.models import Job +from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task + +logger = logging.getLogger(__name__) + + +def find_ancestor_by_parent_chain(taxon, target_rank: str): + """Climb up parent relationships until a taxon with the target rank is found.""" + if not taxon: + return None + + target_rank = target_rank.upper() + + current = taxon + while current: + if current.rank.upper() == target_rank: + return current + current = current.parent + + return None + + +@register_postprocessing_task +class RankRollupTask(BasePostProcessingTask): + """Post-processing task that rolls up low-confidence classifications + to higher ranks using aggregated scores. + """ + + key = "rank_rollup" + name = "Rank Rollup" + + DEFAULT_THRESHOLDS = {"species": 0.8, "genus": 0.6, "family": 0.4} + ROLLUP_ORDER = ["species", "genus", "family"] + + def run(self, job: "Job") -> None: + job.logger.info(f"Running Rank Rollup task for job {job.id}") + + # ---- Read config parameters ---- + config = job.config or {} + collection_id = config.get("source_image_collection_id") + thresholds = config.get("thresholds", self.DEFAULT_THRESHOLDS) + rollup_order = config.get("rollup_order", self.ROLLUP_ORDER) + + if not collection_id: + job.logger.warning("No 'source_image_collection_id' provided in job config. Aborting task.") + return + + job.logger.info(f"Config: collection_id={collection_id}, thresholds={thresholds}, rollup_order={rollup_order}") + + qs = Classification.objects.filter( + terminal=True, + taxon__isnull=False, + detection__source_image__collections__id=collection_id, + ) + + updated_occurrences = [] + + with transaction.atomic(): + for clf in qs: + if not clf.scores or not clf.category_map: + continue + + taxon_scores = defaultdict(float) + + for idx, score in enumerate(clf.scores): + label = clf.category_map.labels[idx] + if not label: + continue + + taxon = Taxon.objects.filter(name=label).first() + if not taxon: + continue + + for rank in rollup_order: + ancestor = find_ancestor_by_parent_chain(taxon, rank) + if ancestor: + taxon_scores[ancestor] += score + + new_taxon = None + new_score = None + for rank in rollup_order: + threshold = thresholds.get(rank, 1.0) + candidates = {t: s for t, s in taxon_scores.items() if t.rank == rank} + if not candidates: + continue + best_taxon, best_score = max(candidates.items(), key=lambda kv: kv[1]) + if best_score >= threshold: + new_taxon, new_score = best_taxon, best_score + break + + if new_taxon and new_taxon != clf.taxon: + with transaction.atomic(): + Classification.objects.filter(detection=clf.detection, terminal=True).update(terminal=False) + + Classification.objects.create( + detection=clf.detection, + taxon=new_taxon, + score=new_score, + terminal=True, + algorithm=clf.algorithm, + timestamp=timezone.now(), + ) + + occurrence = clf.detection.occurrence + if occurrence: + Identification.objects.create( + occurrence=occurrence, + taxon=new_taxon, + user=None, + comment=f"Auto-set by {self.name} post-processing task", + ) + updated_occurrences.append(occurrence.pk) + + job.logger.info( + f"Rolled up occurrence {occurrence.pk}: {clf.taxon} to{new_taxon} " + f"({new_taxon.rank}) with rolled-up score={new_score:.3f}" + ) + + job.logger.info(f"Rank rollup completed. Updated {len(updated_occurrences)} occurrences.") + job.logger.info(f"Rank rollup completed for collection {collection_id}.") From 2e81d9038ac89ea25a14145c7695be78f8f65211 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 7 Oct 2025 10:43:30 -0400 Subject: [PATCH 12/44] feat: add class masking post processing task --- ami/ml/post_processing/class_masking.py | 249 ++++++++++++++++++++++++ 1 file changed, 249 insertions(+) create mode 100644 ami/ml/post_processing/class_masking.py diff --git a/ami/ml/post_processing/class_masking.py b/ami/ml/post_processing/class_masking.py new file mode 100644 index 000000000..7bbf9a0e8 --- /dev/null +++ b/ami/ml/post_processing/class_masking.py @@ -0,0 +1,249 @@ +import logging + +from django.db.models import QuerySet +from django.utils import timezone + +from ami.main.models import Classification, Occurrence, SourceImageCollection, TaxaList +from ami.ml.models import Algorithm, AlgorithmCategoryMap +from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task + +logger = logging.getLogger(__name__) + + +def update_single_occurrence( + occurrence: Occurrence, + algorithm: Algorithm, + taxa_list: TaxaList, + task_logger: logging.Logger = logger, +): + task_logger.info(f"Recalculating classifications for occurrence {occurrence.pk}.") + + # Get the classifications for the occurrence in the collection + classifications = Classification.objects.filter( + detection__occurrence=occurrence, + terminal=True, + algorithm=algorithm, + scores__isnull=False, + ).distinct() + + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=taxa_list, + algorithm=algorithm, + ) + + +def update_occurrences_in_collection( + collection: SourceImageCollection, + taxa_list: TaxaList, + algorithm: Algorithm, + params: dict, + task_logger: logging.Logger = logger, + job=None, +): + task_logger.info(f"Recalculating classifications based on a taxa list. Params: {params}") + + # Make new AlgorithmCategoryMap with the taxa in the list + # @TODO + + classifications = Classification.objects.filter( + detection__source_image__collections=collection, + terminal=True, + # algorithm__task_type="classification", + algorithm=algorithm, + scores__isnull=False, + ).distinct() + + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=taxa_list, + algorithm=algorithm, + ) + + +def make_classifications_filtered_by_taxa_list( + classifications: QuerySet[Classification], + taxa_list: TaxaList, + algorithm: Algorithm, +): + taxa_in_list = taxa_list.taxa.all() + + occurrences_to_update: set[Occurrence] = set() + logger.info(f"Found {len(classifications)} terminal classifications with scores to update.") + + if not classifications: + raise ValueError("No terminal classifications with scores found to update.") + + if not algorithm.category_map: + raise ValueError(f"Algorithm {algorithm} does not have a category map.") + category_map: AlgorithmCategoryMap = algorithm.category_map + + # Consider moving this to a method on the Classification model + + # @TODO find a more efficient way to get the category map with taxa. This is slow! + logger.info(f"Retrieving category map with Taxa instances for algorithm {algorithm}") + category_map_with_taxa = category_map.with_taxa() + # Filter the category map to only include taxa that are in the taxa list + # included_category_map_with_taxa = [ + # category for category in category_map_with_taxa if category["taxon"] in taxa_in_list + # ] + excluded_category_map_with_taxa = [ + category for category in category_map_with_taxa if category["taxon"] not in taxa_in_list + ] + + # included_category_indices = [int(category["index"]) for category in category_map_with_taxa] + excluded_category_indices = [ + int(category["index"]) for category in excluded_category_map_with_taxa # type: ignore + ] + + # Log number of categories in the category map, num included, and num excluded, num classifications to update + logger.info( + f"Category map has {len(category_map_with_taxa)} categories, " + f"{len(excluded_category_map_with_taxa)} categories excluded, " + f"{len(classifications)} classifications to check" + ) + + classifications_to_add = [] + classifications_to_update = [] + + timestamp = timezone.now() + for classification in classifications: + scores, logits = classification.scores, classification.logits + # Set scores and logits to zero if they are not in the filtered category indices + + import numpy as np + + # Assert that all scores & logits are lists of numbers + if not isinstance(scores, list) or not all(isinstance(score, (int, float)) for score in scores): + raise ValueError(f"Scores for classification {classification.pk} are not a list of numbers: {scores}") + if not isinstance(logits, list) or not all(isinstance(logit, (int, float)) for logit in logits): + raise ValueError(f"Logits for classification {classification.pk} are not a list of numbers: {logits}") + + logger.debug(f"Processing classification {classification.pk} with {len(scores)} scores") + logger.info(f"Previous totals: {sum(scores)} scores, {sum(logits)} logits") + + # scores_np_filtered = np.array(scores) + logits_np = np.array(logits) + + # scores_np_filtered[excluded_category_indices] = 0.0 + + # @TODO can we use np.NAN instead of 0.0? zero will NOT calculate correctly in softmax. + # @TODO delete the excluded categories from the scores and logits instead of setting to 0.0 + # logits_np[excluded_category_indices] = 0.0 + # logits_np[excluded_category_indices] = np.nan + logits_np[excluded_category_indices] = -100 + + logits: list[float] = logits_np.tolist() + + from numpy import exp + from numpy import sum as np_sum + + # @TODO add test to see if this is correct, or needed! + # Recalculate the softmax scores based on the filtered logits + scores_np: np.ndarray = exp(logits_np - np.max(logits_np)) # Subtract max for numerical stability + scores_np /= np_sum(scores_np) # Normalize to get probabilities + + scores: list = scores_np.tolist() # Convert back to list + + logger.info(f"New totals: {sum(scores)} scores, {sum(logits)} logits") + + # Get the taxon with the highest score using the index of the max score + top_index = scores.index(max(scores)) + top_taxon = category_map_with_taxa[top_index][ + "taxon" + ] # @TODO: This doesn't work if the taxon has never been classified + print("Top taxon: ", category_map_with_taxa[top_index]) # @TODO: REMOVE + print("Top index: ", top_index) # @TODO: REMOVE + + # check if needs updating + if classification.scores == scores and classification.logits == logits: + logger.debug(f"Classification {classification.pk} does not need updating") + continue + + # Consider the existing classification as an intermediate classification + classification.terminal = False + classification.updated_at = timestamp + + # Recalculate the top taxon and score + new_classification = Classification( + taxon=top_taxon, + algorithm=classification.algorithm, + score=max(scores), + scores=scores, + logits=logits, + detection=classification.detection, + timestamp=classification.timestamp, + terminal=True, + category_map=None, # @TODO need a new category map with the filtered taxa + created_at=timestamp, + updated_at=timestamp, + ) + if new_classification.taxon is None: + raise (ValueError("Classification isn't registered yet. Aborting")) # @TODO remove or fail gracefully + + classifications_to_update.append(classification) + classifications_to_add.append(new_classification) + + assert new_classification.detection is not None + assert new_classification.detection.occurrence is not None + occurrences_to_update.add(new_classification.detection.occurrence) + + logging.info( + f"Adding new classification for Taxon {top_taxon} to occurrence {new_classification.detection.occurrence}" + ) + + # Bulk update the existing classifications + if classifications_to_update: + logger.info(f"Bulk updating {len(classifications_to_update)} existing classifications") + Classification.objects.bulk_update(classifications_to_update, ["terminal", "updated_at"]) + logger.info(f"Updated {len(classifications_to_update)} existing classifications") + + if classifications_to_add: + # Bulk create the new classifications + logger.info(f"Bulk creating {len(classifications_to_add)} new classifications") + Classification.objects.bulk_create(classifications_to_add) + logger.info(f"Added {len(classifications_to_add)} new classifications") + + # Update the occurrence determinations + logger.info(f"Updating the determinations for {len(occurrences_to_update)} occurrences") + for occurrence in occurrences_to_update: + occurrence.save(update_determination=True) + logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") + + +@register_postprocessing_task +class ClassMaskingTask(BasePostProcessingTask): + key = "class_masking" + name = "Class Masking" + + def run(self, job): + self.log_config(job) + + collection_id = self.config.get("collection_id") + taxa_list_id = self.config.get("taxa_list_id") + algorithm_id = self.config.get("algorithm_id") + + if not all([collection_id, taxa_list_id, algorithm_id]): + job.logger.error("Missing required configuration: collection_id, taxa_list_id, algorithm_id") + return + + try: + collection = SourceImageCollection.objects.get(pk=collection_id) + taxa_list = TaxaList.objects.get(pk=taxa_list_id) + algorithm = Algorithm.objects.get(pk=algorithm_id) + except Exception as e: + job.logger.exception(f"Failed to load objects: {e}") + return + + job.logger.info(f"Applying class masking on collection {collection_id} using taxa list {taxa_list_id}") + + update_occurrences_in_collection( + collection=collection, + taxa_list=taxa_list, + algorithm=algorithm, + params=self.config, + task_logger=job.logger, + job=job, + ) + + job.logger.info("Class masking completed successfully.") From 0baf8ce39151a6520708c1063347a3ea9fb59615 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 7 Oct 2025 10:43:57 -0400 Subject: [PATCH 13/44] feat: trigger class masking from admin page --- ami/main/admin.py | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index a3f4d6aae..d7d79aafa 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -641,7 +641,33 @@ def run_small_size_filter(self, request: HttpRequest, queryset: QuerySet[SourceI self.message_user(request, f"Queued Small Size Filter for {queryset.count()} collection(s). Jobs: {jobs}") - actions = [populate_collection, populate_collection_async, run_small_size_filter] + @admin.action(description="Run Class Masking post-processing task (async)") + def run_class_masking(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: + jobs = [] + + DEFAULT_TAXA_LIST_ID = 5 + DEFAULT_ALGORITHM_ID = 11 + + for collection in queryset: + job = Job.objects.create( + name=f"Post-processing: ClassMasking on Collection {collection.pk}", + project=collection.project, + job_type_key="post_processing", + params={ + "task": "class_masking", + "config": { + "collection_id": collection.pk, + "taxa_list_id": DEFAULT_TAXA_LIST_ID, + "algorithm_id": DEFAULT_ALGORITHM_ID, + }, + }, + ) + job.enqueue() + jobs.append(job.pk) + + self.message_user(request, f"Queued Class Masking for {queryset.count()} collection(s). Jobs: {jobs}") + + actions = [populate_collection, populate_collection_async, run_small_size_filter, run_class_masking] # Hide images many-to-many field from form. This would list all source images in the database. exclude = ("images",) From f3caa18c4f93b499527910f6b273d804d279d2c8 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 8 Oct 2025 10:21:31 -0400 Subject: [PATCH 14/44] fix: modified log messages --- ami/ml/post_processing/__init__.py | 2 +- ami/ml/post_processing/rank_rollup.py | 6 +++--- ami/ml/post_processing/small_size_filter.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ami/ml/post_processing/__init__.py b/ami/ml/post_processing/__init__.py index 3517ed47c..bf2e99698 100644 --- a/ami/ml/post_processing/__init__.py +++ b/ami/ml/post_processing/__init__.py @@ -1 +1 @@ -from . import small_size_filter # noqa: F401 +from . import class_masking, rank_rollup, small_size_filter # noqa: F401 diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index 01a315d3e..a66eda0e0 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -3,9 +3,9 @@ from django.db import transaction from django.utils import timezone -from main.models import Classification, Identification, Taxon from ami.jobs.models import Job +from ami.main.models import Classification, Identification, Taxon from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task logger = logging.getLogger(__name__) @@ -40,10 +40,10 @@ class RankRollupTask(BasePostProcessingTask): ROLLUP_ORDER = ["species", "genus", "family"] def run(self, job: "Job") -> None: - job.logger.info(f"Running Rank Rollup task for job {job.id}") + job.logger.info(f"Running Rank Rollup task for job {job.pk}") # ---- Read config parameters ---- - config = job.config or {} + config = self.config or {} collection_id = config.get("source_image_collection_id") thresholds = config.get("thresholds", self.DEFAULT_THRESHOLDS) rollup_order = config.get("rollup_order", self.ROLLUP_ORDER) diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index 784647dd8..3f144aff4 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -32,7 +32,7 @@ def run(self, job: "Job") -> None: try: collection = SourceImageCollection.objects.get(pk=collection_id) - job.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project_id})") + job.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project})") except SourceImageCollection.DoesNotExist: msg = f"SourceImageCollection {collection_id} not found" job.logger.error(msg) @@ -83,7 +83,7 @@ def run(self, job: "Job") -> None: occurrence=det.occurrence, taxon=not_identifiable_taxon, user=None, # since this comes from a post-processing algorithm not a human - comment=f"Auto-set by {self.name} Filter post-processing task", + comment=f"Auto-set by {self.name} post-processing task", ) modified += 1 job.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") From 65d4fef35518e6aa97908e48a513c3f1f5de7c2b Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 8 Oct 2025 10:41:16 -0400 Subject: [PATCH 15/44] fix: set the classification algorithm to the rank rollup Algorithm when creating a terminal classification with the rolled up taxon --- ami/ml/post_processing/rank_rollup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index a66eda0e0..4042612b2 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -104,7 +104,7 @@ def run(self, job: "Job") -> None: taxon=new_taxon, score=new_score, terminal=True, - algorithm=clf.algorithm, + algorithm=self.algorithm, timestamp=timezone.now(), ) From e13afc1205a71b18104604a0565ddf26a6e9a3b7 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 8 Oct 2025 10:41:40 -0400 Subject: [PATCH 16/44] feat: trigger rank rollup from admin page --- ami/main/admin.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index d7d79aafa..4519ae6db 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -667,7 +667,34 @@ def run_class_masking(self, request: HttpRequest, queryset: QuerySet[SourceImage self.message_user(request, f"Queued Class Masking for {queryset.count()} collection(s). Jobs: {jobs}") - actions = [populate_collection, populate_collection_async, run_small_size_filter, run_class_masking] + @admin.action(description="Run Rank Rollup post-processing task (async)") + def run_rank_rollup(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: + """Trigger the Rank Rollup post-processing job asynchronously.""" + jobs = [] + DEFAULT_THRESHOLDS = {"species": 0.8, "genus": 0.6, "family": 0.4} + + for collection in queryset: + job = Job.objects.create( + name=f"Post-processing: RankRollup on Collection {collection.pk}", + project=collection.project, + job_type_key="post_processing", + params={ + "task": "rank_rollup", + "config": {"source_image_collection_id": collection.pk, "thresholds": DEFAULT_THRESHOLDS}, + }, + ) + job.enqueue() + jobs.append(job.pk) + + self.message_user(request, f"Queued Rank Rollup for {queryset.count()} collection(s). Jobs: {jobs}") + + actions = [ + populate_collection, + populate_collection_async, + run_small_size_filter, + run_class_masking, + run_rank_rollup, + ] # Hide images many-to-many field from form. This would list all source images in the database. exclude = ("images",) From 7ecc18cf06cb7c781b483bb4b1102a2ad354a2a4 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:31:41 -0400 Subject: [PATCH 17/44] Remove class_masking.py from framework branch --- ami/ml/post_processing/class_masking.py | 249 ------------------------ 1 file changed, 249 deletions(-) delete mode 100644 ami/ml/post_processing/class_masking.py diff --git a/ami/ml/post_processing/class_masking.py b/ami/ml/post_processing/class_masking.py deleted file mode 100644 index 7bbf9a0e8..000000000 --- a/ami/ml/post_processing/class_masking.py +++ /dev/null @@ -1,249 +0,0 @@ -import logging - -from django.db.models import QuerySet -from django.utils import timezone - -from ami.main.models import Classification, Occurrence, SourceImageCollection, TaxaList -from ami.ml.models import Algorithm, AlgorithmCategoryMap -from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task - -logger = logging.getLogger(__name__) - - -def update_single_occurrence( - occurrence: Occurrence, - algorithm: Algorithm, - taxa_list: TaxaList, - task_logger: logging.Logger = logger, -): - task_logger.info(f"Recalculating classifications for occurrence {occurrence.pk}.") - - # Get the classifications for the occurrence in the collection - classifications = Classification.objects.filter( - detection__occurrence=occurrence, - terminal=True, - algorithm=algorithm, - scores__isnull=False, - ).distinct() - - make_classifications_filtered_by_taxa_list( - classifications=classifications, - taxa_list=taxa_list, - algorithm=algorithm, - ) - - -def update_occurrences_in_collection( - collection: SourceImageCollection, - taxa_list: TaxaList, - algorithm: Algorithm, - params: dict, - task_logger: logging.Logger = logger, - job=None, -): - task_logger.info(f"Recalculating classifications based on a taxa list. Params: {params}") - - # Make new AlgorithmCategoryMap with the taxa in the list - # @TODO - - classifications = Classification.objects.filter( - detection__source_image__collections=collection, - terminal=True, - # algorithm__task_type="classification", - algorithm=algorithm, - scores__isnull=False, - ).distinct() - - make_classifications_filtered_by_taxa_list( - classifications=classifications, - taxa_list=taxa_list, - algorithm=algorithm, - ) - - -def make_classifications_filtered_by_taxa_list( - classifications: QuerySet[Classification], - taxa_list: TaxaList, - algorithm: Algorithm, -): - taxa_in_list = taxa_list.taxa.all() - - occurrences_to_update: set[Occurrence] = set() - logger.info(f"Found {len(classifications)} terminal classifications with scores to update.") - - if not classifications: - raise ValueError("No terminal classifications with scores found to update.") - - if not algorithm.category_map: - raise ValueError(f"Algorithm {algorithm} does not have a category map.") - category_map: AlgorithmCategoryMap = algorithm.category_map - - # Consider moving this to a method on the Classification model - - # @TODO find a more efficient way to get the category map with taxa. This is slow! - logger.info(f"Retrieving category map with Taxa instances for algorithm {algorithm}") - category_map_with_taxa = category_map.with_taxa() - # Filter the category map to only include taxa that are in the taxa list - # included_category_map_with_taxa = [ - # category for category in category_map_with_taxa if category["taxon"] in taxa_in_list - # ] - excluded_category_map_with_taxa = [ - category for category in category_map_with_taxa if category["taxon"] not in taxa_in_list - ] - - # included_category_indices = [int(category["index"]) for category in category_map_with_taxa] - excluded_category_indices = [ - int(category["index"]) for category in excluded_category_map_with_taxa # type: ignore - ] - - # Log number of categories in the category map, num included, and num excluded, num classifications to update - logger.info( - f"Category map has {len(category_map_with_taxa)} categories, " - f"{len(excluded_category_map_with_taxa)} categories excluded, " - f"{len(classifications)} classifications to check" - ) - - classifications_to_add = [] - classifications_to_update = [] - - timestamp = timezone.now() - for classification in classifications: - scores, logits = classification.scores, classification.logits - # Set scores and logits to zero if they are not in the filtered category indices - - import numpy as np - - # Assert that all scores & logits are lists of numbers - if not isinstance(scores, list) or not all(isinstance(score, (int, float)) for score in scores): - raise ValueError(f"Scores for classification {classification.pk} are not a list of numbers: {scores}") - if not isinstance(logits, list) or not all(isinstance(logit, (int, float)) for logit in logits): - raise ValueError(f"Logits for classification {classification.pk} are not a list of numbers: {logits}") - - logger.debug(f"Processing classification {classification.pk} with {len(scores)} scores") - logger.info(f"Previous totals: {sum(scores)} scores, {sum(logits)} logits") - - # scores_np_filtered = np.array(scores) - logits_np = np.array(logits) - - # scores_np_filtered[excluded_category_indices] = 0.0 - - # @TODO can we use np.NAN instead of 0.0? zero will NOT calculate correctly in softmax. - # @TODO delete the excluded categories from the scores and logits instead of setting to 0.0 - # logits_np[excluded_category_indices] = 0.0 - # logits_np[excluded_category_indices] = np.nan - logits_np[excluded_category_indices] = -100 - - logits: list[float] = logits_np.tolist() - - from numpy import exp - from numpy import sum as np_sum - - # @TODO add test to see if this is correct, or needed! - # Recalculate the softmax scores based on the filtered logits - scores_np: np.ndarray = exp(logits_np - np.max(logits_np)) # Subtract max for numerical stability - scores_np /= np_sum(scores_np) # Normalize to get probabilities - - scores: list = scores_np.tolist() # Convert back to list - - logger.info(f"New totals: {sum(scores)} scores, {sum(logits)} logits") - - # Get the taxon with the highest score using the index of the max score - top_index = scores.index(max(scores)) - top_taxon = category_map_with_taxa[top_index][ - "taxon" - ] # @TODO: This doesn't work if the taxon has never been classified - print("Top taxon: ", category_map_with_taxa[top_index]) # @TODO: REMOVE - print("Top index: ", top_index) # @TODO: REMOVE - - # check if needs updating - if classification.scores == scores and classification.logits == logits: - logger.debug(f"Classification {classification.pk} does not need updating") - continue - - # Consider the existing classification as an intermediate classification - classification.terminal = False - classification.updated_at = timestamp - - # Recalculate the top taxon and score - new_classification = Classification( - taxon=top_taxon, - algorithm=classification.algorithm, - score=max(scores), - scores=scores, - logits=logits, - detection=classification.detection, - timestamp=classification.timestamp, - terminal=True, - category_map=None, # @TODO need a new category map with the filtered taxa - created_at=timestamp, - updated_at=timestamp, - ) - if new_classification.taxon is None: - raise (ValueError("Classification isn't registered yet. Aborting")) # @TODO remove or fail gracefully - - classifications_to_update.append(classification) - classifications_to_add.append(new_classification) - - assert new_classification.detection is not None - assert new_classification.detection.occurrence is not None - occurrences_to_update.add(new_classification.detection.occurrence) - - logging.info( - f"Adding new classification for Taxon {top_taxon} to occurrence {new_classification.detection.occurrence}" - ) - - # Bulk update the existing classifications - if classifications_to_update: - logger.info(f"Bulk updating {len(classifications_to_update)} existing classifications") - Classification.objects.bulk_update(classifications_to_update, ["terminal", "updated_at"]) - logger.info(f"Updated {len(classifications_to_update)} existing classifications") - - if classifications_to_add: - # Bulk create the new classifications - logger.info(f"Bulk creating {len(classifications_to_add)} new classifications") - Classification.objects.bulk_create(classifications_to_add) - logger.info(f"Added {len(classifications_to_add)} new classifications") - - # Update the occurrence determinations - logger.info(f"Updating the determinations for {len(occurrences_to_update)} occurrences") - for occurrence in occurrences_to_update: - occurrence.save(update_determination=True) - logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") - - -@register_postprocessing_task -class ClassMaskingTask(BasePostProcessingTask): - key = "class_masking" - name = "Class Masking" - - def run(self, job): - self.log_config(job) - - collection_id = self.config.get("collection_id") - taxa_list_id = self.config.get("taxa_list_id") - algorithm_id = self.config.get("algorithm_id") - - if not all([collection_id, taxa_list_id, algorithm_id]): - job.logger.error("Missing required configuration: collection_id, taxa_list_id, algorithm_id") - return - - try: - collection = SourceImageCollection.objects.get(pk=collection_id) - taxa_list = TaxaList.objects.get(pk=taxa_list_id) - algorithm = Algorithm.objects.get(pk=algorithm_id) - except Exception as e: - job.logger.exception(f"Failed to load objects: {e}") - return - - job.logger.info(f"Applying class masking on collection {collection_id} using taxa list {taxa_list_id}") - - update_occurrences_in_collection( - collection=collection, - taxa_list=taxa_list, - algorithm=algorithm, - params=self.config, - task_logger=job.logger, - job=job, - ) - - job.logger.info("Class masking completed successfully.") From f2140256f8bc620944d5588ece43fc2bee4722be Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:38:46 -0400 Subject: [PATCH 18/44] fix: initialize post-processing tasks with job context and simplify run() call --- ami/jobs/models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ami/jobs/models.py b/ami/jobs/models.py index 39e7a0ff3..96470a97d 100644 --- a/ami/jobs/models.py +++ b/ami/jobs/models.py @@ -668,8 +668,8 @@ def run(cls, job: "Job"): if not task_cls: raise ValueError(f"Unknown post-processing task '{task_key}'") - task = task_cls(**config) - task.run(job) + task = task_cls(job=job, **config) + task.run() job.progress.update_stage(cls.key, status=JobState.SUCCESS, progress=1) job.finished_at = datetime.datetime.now() job.update_status(JobState.SUCCESS) From 20ff4b6b3f5b555bae81b7510f6cf05173cd642d Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:39:07 -0400 Subject: [PATCH 19/44] feat: add permission to run post-processing jobs --- ami/main/models.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ami/main/models.py b/ami/main/models.py index 7e3935609..bab6b3a20 100644 --- a/ami/main/models.py +++ b/ami/main/models.py @@ -314,6 +314,7 @@ class Permissions: RUN_POPULATE_CAPTURES_COLLECTION_JOB = "run_populate_captures_collection_job" RUN_DATA_STORAGE_SYNC_JOB = "run_data_storage_sync_job" RUN_DATA_EXPORT_JOB = "run_data_export_job" + RUN_POST_PROCESSING_JOB = "run_post_processing_job" DELETE_JOB = "delete_job" # Deployment permissions @@ -376,6 +377,7 @@ class Meta: ("run_data_storage_sync_job", "Can run/retry/cancel Data Storage Sync jobs"), ("run_data_export_job", "Can run/retry/cancel Data Export jobs"), ("run_single_image_ml_job", "Can process a single capture"), + ("run_post_processing_job", "Can run/retry/cancel Post-Processing jobs"), ("delete_job", "Can delete a job"), # Deployment permissions ("create_deployment", "Can create a deployment"), From 5b66ae3033dbad38fa0b70217dfac42f14474747 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:39:41 -0400 Subject: [PATCH 20/44] chore: remove class_masking import --- ami/ml/post_processing/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ami/ml/post_processing/__init__.py b/ami/ml/post_processing/__init__.py index bf2e99698..136481120 100644 --- a/ami/ml/post_processing/__init__.py +++ b/ami/ml/post_processing/__init__.py @@ -1 +1 @@ -from . import class_masking, rank_rollup, small_size_filter # noqa: F401 +from . import rank_rollup, small_size_filter # noqa: F401 From 0419eff07909dc56467f6de2667edf8103ac25bb Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:40:29 -0400 Subject: [PATCH 21/44] refactor: redesign BasePostProcessingTask with job-aware logging, progress, and algorithm binding --- ami/ml/post_processing/base.py | 65 ++++++++++++++++++++-------------- 1 file changed, 39 insertions(+), 26 deletions(-) diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py index baa1c452e..f294b9aab 100644 --- a/ami/ml/post_processing/base.py +++ b/ami/ml/post_processing/base.py @@ -23,19 +23,6 @@ def register_postprocessing_task(task_cls: type["BasePostProcessingTask"]): # Register the task POSTPROCESSING_TASKS[task_cls.key] = task_cls - - # Ensure Algorithm object exists for this task - algorithm, _ = Algorithm.objects.get_or_create( - name=task_cls.__name__, - defaults={ - "description": f"Post-processing task: {task_cls.key}", - "task_type": AlgorithmTaskType.POST_PROCESSING.value, - }, - ) - - # Attach the Algorithm object to the task class - task_cls.algorithm = algorithm - return task_cls @@ -56,24 +43,50 @@ class BasePostProcessingTask(abc.ABC): key: str = "" name: str = "" - def __init__(self, **config: Any): + def __init__( + self, + job: Job | None = None, + logger: logging.Logger | None = None, + **config: Any, + ): + self.job = job + self.config = config + # Choose the right logger + if logger is not None: + self.logger = logger + elif job is not None: + self.logger = job.logger + else: + self.logger = logging.getLogger(f"ami.post_processing.{self.key}") + + algorithm, _ = Algorithm.objects.get_or_create( + name=self.__class__.__name__, + defaults={ + "description": f"Post-processing task: {self.key}", + "task_type": AlgorithmTaskType.POST_PROCESSING.value, + }, + ) + self.algorithm: Algorithm = algorithm + + self.logger.info(f"Initialized {self.__class__.__name__} with config={self.config}, job={job}") + + def update_progress(self, progress: float): """ - Initialize task with configuration parameters. + Update progress if job is present, otherwise just log. """ - self.config: dict[str, Any] = config - self.logger = logging.getLogger(f"ami.post_processing.{self.key}") + + if self.job: + self.job.progress.update_stage(self.job.job_type_key, progress=progress) + self.job.save(update_fields=["progress"]) + + else: + # No job object — fallback to plain logging + self.logger.info(f"[{self.name}] Progress {progress:.0%}") @abc.abstractmethod - def run(self, job: Job) -> None: + def run(self) -> None: """ Run the task logic. Must be implemented by subclasses. - The job parameter provides context (project, logs, etc.). - """ - raise NotImplementedError("Subclasses must implement run()") - - def log_config(self, job: Job): - """ - Helper to log the task configuration at start. """ - job.logger.info(f"Running task {self.name} ({self.key}) with config: {self.config}") + raise NotImplementedError("BasePostProcessingTask subclasses must implement run()") From 1ad1e765237d9b07763edaf06b1bfd7450dcc679 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:41:16 -0400 Subject: [PATCH 22/44] refactor: adapt RankRollupTask to new BasePostProcessingTask with self.logger and progress updates --- ami/ml/post_processing/rank_rollup.py | 55 ++++++++++++++++++++------- 1 file changed, 42 insertions(+), 13 deletions(-) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index 4042612b2..9f1e3b8f8 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -4,7 +4,6 @@ from django.db import transaction from django.utils import timezone -from ami.jobs.models import Job from ami.main.models import Classification, Identification, Taxon from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task @@ -34,13 +33,14 @@ class RankRollupTask(BasePostProcessingTask): """ key = "rank_rollup" - name = "Rank Rollup" + name = "Rank rollup" DEFAULT_THRESHOLDS = {"species": 0.8, "genus": 0.6, "family": 0.4} ROLLUP_ORDER = ["species", "genus", "family"] - def run(self, job: "Job") -> None: - job.logger.info(f"Running Rank Rollup task for job {job.pk}") + def run(self) -> None: + job = self.job + self.logger.info(f"Starting {self.name} task for job {job.pk if job else 'N/A'}") # ---- Read config parameters ---- config = self.config or {} @@ -49,10 +49,12 @@ def run(self, job: "Job") -> None: rollup_order = config.get("rollup_order", self.ROLLUP_ORDER) if not collection_id: - job.logger.warning("No 'source_image_collection_id' provided in job config. Aborting task.") + self.logger.warning("No 'source_image_collection_id' provided in config. Aborting task.") return - job.logger.info(f"Config: collection_id={collection_id}, thresholds={thresholds}, rollup_order={rollup_order}") + self.logger.info( + f"Config loaded: collection_id={collection_id}, thresholds={thresholds}, rollup_order={rollup_order}" + ) qs = Classification.objects.filter( terminal=True, @@ -60,11 +62,20 @@ def run(self, job: "Job") -> None: detection__source_image__collections__id=collection_id, ) + total = qs.count() + self.logger.info(f"Found {total} terminal classifications to process for collection {collection_id}") + updated_occurrences = [] with transaction.atomic(): - for clf in qs: - if not clf.scores or not clf.category_map: + for i, clf in enumerate(qs.iterator(), start=1): + self.logger.info(f"Processing classification #{clf.pk} (taxon={clf.taxon}, score={clf.score:.3f})") + + if not clf.scores: + self.logger.warning(f"Skipping classification #{clf.pk}: no scores available") + continue + if not clf.category_map: + self.logger.warning(f"Skipping classification #{clf.pk}: no category_map assigned") continue taxon_scores = defaultdict(float) @@ -76,29 +87,40 @@ def run(self, job: "Job") -> None: taxon = Taxon.objects.filter(name=label).first() if not taxon: + self.logger.debug(f"Skipping label '{label}' (no matching Taxon in DB)") continue for rank in rollup_order: ancestor = find_ancestor_by_parent_chain(taxon, rank) if ancestor: taxon_scores[ancestor] += score + self.logger.debug(f" + Added {score:.3f} to ancestor {ancestor.name} ({rank})") new_taxon = None new_score = None for rank in rollup_order: threshold = thresholds.get(rank, 1.0) candidates = {t: s for t, s in taxon_scores.items() if t.rank == rank} + if not candidates: + self.logger.debug(f"No candidates found at rank {rank}") continue + best_taxon, best_score = max(candidates.items(), key=lambda kv: kv[1]) + self.logger.debug( + f"Best at rank {rank}: {best_taxon.name} ({best_score:.3f}) [threshold={threshold}]" + ) + if best_score >= threshold: new_taxon, new_score = best_taxon, best_score + self.logger.info(f"Rollup decision: {new_taxon.name} ({rank}) with score {new_score:.3f}") break if new_taxon and new_taxon != clf.taxon: + self.logger.info(f"Rolling up {clf.taxon} → {new_taxon} ({new_taxon.rank})") + with transaction.atomic(): Classification.objects.filter(detection=clf.detection, terminal=True).update(terminal=False) - Classification.objects.create( detection=clf.detection, taxon=new_taxon, @@ -118,10 +140,17 @@ def run(self, job: "Job") -> None: ) updated_occurrences.append(occurrence.pk) - job.logger.info( - f"Rolled up occurrence {occurrence.pk}: {clf.taxon} to{new_taxon} " + self.logger.info( + f"Rolled up occurrence {occurrence.pk}: {clf.taxon} → {new_taxon} " f"({new_taxon.rank}) with rolled-up score={new_score:.3f}" ) + else: + self.logger.info(f"No rollup applied for classification #{clf.pk} (taxon={clf.taxon})") + + # 🔹 Periodic progress updates + if i % 50 == 0 or i == total: + progress = i / total if total > 0 else 1.0 + self.update_progress(progress) - job.logger.info(f"Rank rollup completed. Updated {len(updated_occurrences)} occurrences.") - job.logger.info(f"Rank rollup completed for collection {collection_id}.") + self.logger.info(f"Rank rollup completed. Updated {len(updated_occurrences)} occurrences.") + self.logger.info(f"{self.name} task finished for collection {collection_id}.") From d97e8e0e031165adfe9a2e61f3c6a99eec809e76 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:41:53 -0400 Subject: [PATCH 23/44] refactor: update SmallSizeFilter to use BasePostProcessingTask logging and progress tracking --- ami/ml/post_processing/small_size_filter.py | 42 ++++++++++----------- 1 file changed, 19 insertions(+), 23 deletions(-) diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index 3f144aff4..a8f9b9010 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -1,7 +1,6 @@ from django.db import transaction from django.utils import timezone -from ami.jobs.models import Job from ami.main.models import Detection, Identification, SourceImageCollection, Taxon, TaxonRank from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task @@ -9,13 +8,13 @@ @register_postprocessing_task class SmallSizeFilter(BasePostProcessingTask): key = "small_size_filter" - name = "Small Size Filter" + name = "Small size filter" - def run(self, job: "Job") -> None: + def run(self) -> None: threshold = self.config.get("size_threshold", 0.01) collection_id = self.config.get("source_image_collection_id") - # Get or create the "Not identifiable" taxon + # Get or create the "Not identifiable" taxon not_identifiable_taxon, _ = Taxon.objects.get_or_create( name="Not identifiable", defaults={ @@ -23,43 +22,43 @@ def run(self, job: "Job") -> None: "notes": "Auto-generated taxon for small size filter", }, ) - job.logger.info(f"=== Starting {self.name} ===") + self.logger.info(f"=== Starting {self.name} ===") if not collection_id: msg = "Missing required config param: source_image_collection_id" - job.logger.error(msg) + self.logger.error(msg) raise ValueError(msg) try: collection = SourceImageCollection.objects.get(pk=collection_id) - job.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project})") + self.logger.info(f"Loaded SourceImageCollection {collection_id} (Project={collection.project})") except SourceImageCollection.DoesNotExist: msg = f"SourceImageCollection {collection_id} not found" - job.logger.error(msg) + self.logger.error(msg) raise ValueError(msg) detections = Detection.objects.filter(source_image__collections=collection) total = detections.count() - job.logger.info(f"Found {total} detections in collection {collection_id}") + self.logger.info(f"Found {total} detections in collection {collection_id}") modified = 0 - for det in detections.iterator(): + for i, det in enumerate(detections.iterator(), start=1): bbox = det.get_bbox() if not bbox: - job.logger.debug(f"Detection {det.pk}: no bbox, skipping") + self.logger.debug(f"Detection {det.pk}: no bbox, skipping") continue img_w, img_h = det.source_image.width, det.source_image.height if not img_w or not img_h: - job.logger.debug(f"Detection {det.pk}: missing source image dims, skipping") + self.logger.debug(f"Detection {det.pk}: missing source image dims, skipping") continue det_area = det.width() * det.height() img_area = img_w * img_h rel_area = det_area / img_area if img_area else 0 - job.logger.debug( + self.logger.debug( f"Detection {det.pk}: area={det_area}, rel_area={rel_area:.4f}, " f"threshold={threshold:.4f}" ) @@ -82,18 +81,15 @@ def run(self, job: "Job") -> None: Identification.objects.create( occurrence=det.occurrence, taxon=not_identifiable_taxon, - user=None, # since this comes from a post-processing algorithm not a human + user=None, # auto-generated by post-processing comment=f"Auto-set by {self.name} post-processing task", ) modified += 1 - job.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") + self.logger.info(f"Detection {det.pk}: marked as 'Not identifiable'") - job.logger.info(f"=== Completed {self.name}: {modified}/{total} detections modified ===") + # Update progress every 10 detections + if i % 10 == 0 or i == total: + progress = i / total if total > 0 else 1.0 + self.update_progress(progress) - job.result = { - "detections_total": total, - "detections_modified": modified, - "threshold": threshold, - "collection_id": collection_id, - } - job.save(update_fields=["result"]) + self.logger.info(f"=== Completed {self.name}: {modified}/{total} detections modified ===") From 2922c86c1a0044f5630d076464320abccb4983a3 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:43:24 -0400 Subject: [PATCH 24/44] migrations: update Project options to include post-processing job permission --- .../migrations/0075_alter_project_options.py | 59 +++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 ami/main/migrations/0075_alter_project_options.py diff --git a/ami/main/migrations/0075_alter_project_options.py b/ami/main/migrations/0075_alter_project_options.py new file mode 100644 index 000000000..618ba1753 --- /dev/null +++ b/ami/main/migrations/0075_alter_project_options.py @@ -0,0 +1,59 @@ +# Generated by Django 4.2.10 on 2025-10-14 05:01 + +from django.db import migrations + + +class Migration(migrations.Migration): + dependencies = [ + ("main", "0074_taxon_cover_image_credit_taxon_cover_image_url_and_more"), + ] + + operations = [ + migrations.AlterModelOptions( + name="project", + options={ + "ordering": ["-priority", "created_at"], + "permissions": [ + ("create_identification", "Can create identifications"), + ("update_identification", "Can update identifications"), + ("delete_identification", "Can delete identifications"), + ("create_job", "Can create a job"), + ("update_job", "Can update a job"), + ("run_ml_job", "Can run/retry/cancel ML jobs"), + ("run_populate_captures_collection_job", "Can run/retry/cancel Populate Collection jobs"), + ("run_data_storage_sync_job", "Can run/retry/cancel Data Storage Sync jobs"), + ("run_data_export_job", "Can run/retry/cancel Data Export jobs"), + ("run_single_image_ml_job", "Can process a single capture"), + ("run_post_processing_job", "Can run/retry/cancel Post-Processing jobs"), + ("delete_job", "Can delete a job"), + ("create_deployment", "Can create a deployment"), + ("delete_deployment", "Can delete a deployment"), + ("update_deployment", "Can update a deployment"), + ("sync_deployment", "Can sync images to a deployment"), + ("create_sourceimagecollection", "Can create a collection"), + ("update_sourceimagecollection", "Can update a collection"), + ("delete_sourceimagecollection", "Can delete a collection"), + ("populate_sourceimagecollection", "Can populate a collection"), + ("create_sourceimage", "Can create a source image"), + ("update_sourceimage", "Can update a source image"), + ("delete_sourceimage", "Can delete a source image"), + ("star_sourceimage", "Can star a source image"), + ("create_sourceimageupload", "Can create a source image upload"), + ("update_sourceimageupload", "Can update a source image upload"), + ("delete_sourceimageupload", "Can delete a source image upload"), + ("create_s3storagesource", "Can create storage"), + ("delete_s3storagesource", "Can delete storage"), + ("update_s3storagesource", "Can update storage"), + ("test_s3storagesource", "Can test storage connection"), + ("create_site", "Can create a site"), + ("delete_site", "Can delete a site"), + ("update_site", "Can update a site"), + ("create_device", "Can create a device"), + ("delete_device", "Can delete a device"), + ("update_device", "Can update a device"), + ("view_private_data", "Can view private data"), + ("trigger_exports", "Can trigger data exports"), + ], + }, + ), + ] From 9012d7f365a661803dc8b86ffd01fb83d8c1b713 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 11:43:41 -0400 Subject: [PATCH 25/44] migrations: update Algorithm.task_type choices to include post-processing --- .../0025_alter_algorithm_task_type.py | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 ami/ml/migrations/0025_alter_algorithm_task_type.py diff --git a/ami/ml/migrations/0025_alter_algorithm_task_type.py b/ami/ml/migrations/0025_alter_algorithm_task_type.py new file mode 100644 index 000000000..6fc4e8167 --- /dev/null +++ b/ami/ml/migrations/0025_alter_algorithm_task_type.py @@ -0,0 +1,42 @@ +# Generated by Django 4.2.10 on 2025-10-14 05:01 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + dependencies = [ + ("ml", "0024_fix_classifications_missing_category_maps"), + ] + + operations = [ + migrations.AlterField( + model_name="algorithm", + name="task_type", + field=models.CharField( + choices=[ + ("detection", "Detection"), + ("localization", "Localization"), + ("segmentation", "Segmentation"), + ("classification", "Classification"), + ("embedding", "Embedding"), + ("tracking", "Tracking"), + ("tagging", "Tagging"), + ("regression", "Regression"), + ("captioning", "Captioning"), + ("generation", "Generation"), + ("translation", "Translation"), + ("summarization", "Summarization"), + ("question_answering", "Question Answering"), + ("depth_estimation", "Depth Estimation"), + ("pose_estimation", "Pose Estimation"), + ("size_estimation", "Size Estimation"), + ("post_processing", "Post Processing"), + ("other", "Other"), + ("unknown", "Unknown"), + ], + default="unknown", + max_length=255, + null=True, + ), + ), + ] From 787ac0ba4fcf4782bccc3633d5e9b267d8e97bbe Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 14:27:20 -0400 Subject: [PATCH 26/44] migrations: merged migrations --- ami/main/migrations/0077_merge_20251014_1426.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 ami/main/migrations/0077_merge_20251014_1426.py diff --git a/ami/main/migrations/0077_merge_20251014_1426.py b/ami/main/migrations/0077_merge_20251014_1426.py new file mode 100644 index 000000000..7826482e0 --- /dev/null +++ b/ami/main/migrations/0077_merge_20251014_1426.py @@ -0,0 +1,12 @@ +# Generated by Django 4.2.10 on 2025-10-14 14:26 + +from django.db import migrations + + +class Migration(migrations.Migration): + dependencies = [ + ("main", "0075_alter_project_options"), + ("main", "0076_add_occurrence_composite_indexes"), + ] + + operations = [] From 5e85b75ddf18382a884f94620873145730585ec8 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Fri, 10 Oct 2025 10:58:51 -0400 Subject: [PATCH 27/44] refactor: refactor job runner to initialize post-processing tasks with job context --- ami/jobs/models.py | 7 ++- ami/ml/post_processing/base.py | 56 +++++++-------------- ami/ml/post_processing/small_size_filter.py | 9 +--- 3 files changed, 25 insertions(+), 47 deletions(-) diff --git a/ami/jobs/models.py b/ami/jobs/models.py index 96470a97d..27d8f5946 100644 --- a/ami/jobs/models.py +++ b/ami/jobs/models.py @@ -662,13 +662,16 @@ def run(cls, job: "Job"): params = job.params or {} task_key: str = params.get("task", "") config = params.get("config", {}) - job.logger.info(f"Post-processing task: {task_key} with params: {job.params}") - + job.logger.info(f"Post-processing task: {task_key} with params: {config}") + # Get the registered task class task_cls = get_postprocessing_task(task_key) if not task_cls: raise ValueError(f"Unknown post-processing task '{task_key}'") + # Instantiate the task with job context and config task = task_cls(job=job, **config) + + # Run the task task.run() job.progress.update_stage(cls.key, status=JobState.SUCCESS, progress=1) job.finished_at = datetime.datetime.now() diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py index f294b9aab..bd1e8b0e2 100644 --- a/ami/ml/post_processing/base.py +++ b/ami/ml/post_processing/base.py @@ -2,7 +2,7 @@ import abc import logging -from typing import Any +from typing import Any, Optional from ami.jobs.models import Job from ami.ml.models import Algorithm @@ -39,54 +39,34 @@ class BasePostProcessingTask(abc.ABC): Abstract base class for all post-processing tasks. """ - # Each task must override these key: str = "" name: str = "" def __init__( self, - job: Job | None = None, - logger: logging.Logger | None = None, + job: Optional["Job"] = None, + task_logger: logging.Logger | None = None, **config: Any, ): - self.job = job - self.config = config - # Choose the right logger - if logger is not None: - self.logger = logger - elif job is not None: - self.logger = job.logger - else: - self.logger = logging.getLogger(f"ami.post_processing.{self.key}") - - algorithm, _ = Algorithm.objects.get_or_create( - name=self.__class__.__name__, - defaults={ - "description": f"Post-processing task: {self.key}", - "task_type": AlgorithmTaskType.POST_PROCESSING.value, - }, - ) - self.algorithm: Algorithm = algorithm - - self.logger.info(f"Initialized {self.__class__.__name__} with config={self.config}, job={job}") - - def update_progress(self, progress: float): """ - Update progress if job is present, otherwise just log. + Initialize task with optional job and logger context. """ + self.job = job + self.config: dict[str, Any] = config - if self.job: - self.job.progress.update_stage(self.job.job_type_key, progress=progress) - self.job.save(update_fields=["progress"]) - + if job: + self.logger = job.logger + elif task_logger: + self.logger = task_logger else: - # No job object — fallback to plain logging - self.logger.info(f"[{self.name}] Progress {progress:.0%}") + self.logger = logging.getLogger(f"ami.post_processing.{self.key}") + self.log_config() @abc.abstractmethod def run(self) -> None: - """ - Run the task logic. - Must be implemented by subclasses. - """ - raise NotImplementedError("BasePostProcessingTask subclasses must implement run()") + """Run the task logic. Must be implemented by subclasses.""" + raise NotImplementedError("Subclasses must implement run()") + + def log_config(self): + """Helper to log the task configuration at start.""" + self.logger.info(f"Running task {self.name} ({self.key}) with config: {self.config}") diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index a8f9b9010..33f9901b9 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -31,7 +31,7 @@ def run(self) -> None: try: collection = SourceImageCollection.objects.get(pk=collection_id) - self.logger.info(f"Loaded SourceImageCollection {collection_id} (Project={collection.project})") + self.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project})") except SourceImageCollection.DoesNotExist: msg = f"SourceImageCollection {collection_id} not found" self.logger.error(msg) @@ -85,11 +85,6 @@ def run(self) -> None: comment=f"Auto-set by {self.name} post-processing task", ) modified += 1 - self.logger.info(f"Detection {det.pk}: marked as 'Not identifiable'") - - # Update progress every 10 detections - if i % 10 == 0 or i == total: - progress = i / total if total > 0 else 1.0 - self.update_progress(progress) + self.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") self.logger.info(f"=== Completed {self.name}: {modified}/{total} detections modified ===") From 88ffba8fb7e03c12aa2513cf79112cb790f301aa Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 14:52:36 -0400 Subject: [PATCH 28/44] chore: rebase feat/postprocessing-class-masking onto feat/postprocessing-framework --- ami/ml/post_processing/__init__.py | 2 +- ami/ml/post_processing/base.py | 56 +++-- ami/ml/post_processing/class_masking.py | 253 ++++++++++++++++++++ ami/ml/post_processing/small_size_filter.py | 9 +- 4 files changed, 299 insertions(+), 21 deletions(-) create mode 100644 ami/ml/post_processing/class_masking.py diff --git a/ami/ml/post_processing/__init__.py b/ami/ml/post_processing/__init__.py index 136481120..bf2e99698 100644 --- a/ami/ml/post_processing/__init__.py +++ b/ami/ml/post_processing/__init__.py @@ -1 +1 @@ -from . import rank_rollup, small_size_filter # noqa: F401 +from . import class_masking, rank_rollup, small_size_filter # noqa: F401 diff --git a/ami/ml/post_processing/base.py b/ami/ml/post_processing/base.py index bd1e8b0e2..f294b9aab 100644 --- a/ami/ml/post_processing/base.py +++ b/ami/ml/post_processing/base.py @@ -2,7 +2,7 @@ import abc import logging -from typing import Any, Optional +from typing import Any from ami.jobs.models import Job from ami.ml.models import Algorithm @@ -39,34 +39,54 @@ class BasePostProcessingTask(abc.ABC): Abstract base class for all post-processing tasks. """ + # Each task must override these key: str = "" name: str = "" def __init__( self, - job: Optional["Job"] = None, - task_logger: logging.Logger | None = None, + job: Job | None = None, + logger: logging.Logger | None = None, **config: Any, ): - """ - Initialize task with optional job and logger context. - """ self.job = job - self.config: dict[str, Any] = config - - if job: + self.config = config + # Choose the right logger + if logger is not None: + self.logger = logger + elif job is not None: self.logger = job.logger - elif task_logger: - self.logger = task_logger else: self.logger = logging.getLogger(f"ami.post_processing.{self.key}") - self.log_config() + + algorithm, _ = Algorithm.objects.get_or_create( + name=self.__class__.__name__, + defaults={ + "description": f"Post-processing task: {self.key}", + "task_type": AlgorithmTaskType.POST_PROCESSING.value, + }, + ) + self.algorithm: Algorithm = algorithm + + self.logger.info(f"Initialized {self.__class__.__name__} with config={self.config}, job={job}") + + def update_progress(self, progress: float): + """ + Update progress if job is present, otherwise just log. + """ + + if self.job: + self.job.progress.update_stage(self.job.job_type_key, progress=progress) + self.job.save(update_fields=["progress"]) + + else: + # No job object — fallback to plain logging + self.logger.info(f"[{self.name}] Progress {progress:.0%}") @abc.abstractmethod def run(self) -> None: - """Run the task logic. Must be implemented by subclasses.""" - raise NotImplementedError("Subclasses must implement run()") - - def log_config(self): - """Helper to log the task configuration at start.""" - self.logger.info(f"Running task {self.name} ({self.key}) with config: {self.config}") + """ + Run the task logic. + Must be implemented by subclasses. + """ + raise NotImplementedError("BasePostProcessingTask subclasses must implement run()") diff --git a/ami/ml/post_processing/class_masking.py b/ami/ml/post_processing/class_masking.py new file mode 100644 index 000000000..04c621830 --- /dev/null +++ b/ami/ml/post_processing/class_masking.py @@ -0,0 +1,253 @@ +import logging + +from django.db.models import QuerySet +from django.utils import timezone + +from ami.main.models import Classification, Occurrence, SourceImageCollection, TaxaList +from ami.ml.models import Algorithm, AlgorithmCategoryMap +from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task + +logger = logging.getLogger(__name__) + + +def update_single_occurrence( + occurrence: Occurrence, + algorithm: Algorithm, + taxa_list: TaxaList, + task_logger: logging.Logger = logger, +): + task_logger.info(f"Recalculating classifications for occurrence {occurrence.pk}.") + + # Get the classifications for the occurrence in the collection + classifications = Classification.objects.filter( + detection__occurrence=occurrence, + terminal=True, + algorithm=algorithm, + scores__isnull=False, + ).distinct() + + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=taxa_list, + algorithm=algorithm, + ) + + +def update_occurrences_in_collection( + collection: SourceImageCollection, + taxa_list: TaxaList, + algorithm: Algorithm, + params: dict, + task_logger: logging.Logger = logger, + job=None, +): + task_logger.info(f"Recalculating classifications based on a taxa list. Params: {params}") + + # Make new AlgorithmCategoryMap with the taxa in the list + # @TODO + + classifications = Classification.objects.filter( + detection__source_image__collections=collection, + terminal=True, + # algorithm__task_type="classification", + algorithm=algorithm, + scores__isnull=False, + ).distinct() + + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=taxa_list, + algorithm=algorithm, + ) + + +def make_classifications_filtered_by_taxa_list( + classifications: QuerySet[Classification], + taxa_list: TaxaList, + algorithm: Algorithm, +): + taxa_in_list = taxa_list.taxa.all() + + occurrences_to_update: set[Occurrence] = set() + logger.info(f"Found {len(classifications)} terminal classifications with scores to update.") + + if not classifications: + raise ValueError("No terminal classifications with scores found to update.") + + if not algorithm.category_map: + raise ValueError(f"Algorithm {algorithm} does not have a category map.") + category_map: AlgorithmCategoryMap = algorithm.category_map + + # Consider moving this to a method on the Classification model + + # @TODO find a more efficient way to get the category map with taxa. This is slow! + logger.info(f"Retrieving category map with Taxa instances for algorithm {algorithm}") + category_map_with_taxa = category_map.with_taxa() + # Filter the category map to only include taxa that are in the taxa list + # included_category_map_with_taxa = [ + # category for category in category_map_with_taxa if category["taxon"] in taxa_in_list + # ] + excluded_category_map_with_taxa = [ + category for category in category_map_with_taxa if category["taxon"] not in taxa_in_list + ] + + # included_category_indices = [int(category["index"]) for category in category_map_with_taxa] + excluded_category_indices = [ + int(category["index"]) for category in excluded_category_map_with_taxa # type: ignore + ] + + # Log number of categories in the category map, num included, and num excluded, num classifications to update + logger.info( + f"Category map has {len(category_map_with_taxa)} categories, " + f"{len(excluded_category_map_with_taxa)} categories excluded, " + f"{len(classifications)} classifications to check" + ) + + classifications_to_add = [] + classifications_to_update = [] + + timestamp = timezone.now() + for classification in classifications: + scores, logits = classification.scores, classification.logits + # Set scores and logits to zero if they are not in the filtered category indices + + import numpy as np + + # Assert that all scores & logits are lists of numbers + if not isinstance(scores, list) or not all(isinstance(score, (int, float)) for score in scores): + raise ValueError(f"Scores for classification {classification.pk} are not a list of numbers: {scores}") + if not isinstance(logits, list) or not all(isinstance(logit, (int, float)) for logit in logits): + raise ValueError(f"Logits for classification {classification.pk} are not a list of numbers: {logits}") + + logger.debug(f"Processing classification {classification.pk} with {len(scores)} scores") + logger.info(f"Previous totals: {sum(scores)} scores, {sum(logits)} logits") + + # scores_np_filtered = np.array(scores) + logits_np = np.array(logits) + + # scores_np_filtered[excluded_category_indices] = 0.0 + + # @TODO can we use np.NAN instead of 0.0? zero will NOT calculate correctly in softmax. + # @TODO delete the excluded categories from the scores and logits instead of setting to 0.0 + # logits_np[excluded_category_indices] = 0.0 + # logits_np[excluded_category_indices] = np.nan + logits_np[excluded_category_indices] = -100 + + logits: list[float] = logits_np.tolist() + + from numpy import exp + from numpy import sum as np_sum + + # @TODO add test to see if this is correct, or needed! + # Recalculate the softmax scores based on the filtered logits + scores_np: np.ndarray = exp(logits_np - np.max(logits_np)) # Subtract max for numerical stability + scores_np /= np_sum(scores_np) # Normalize to get probabilities + + scores: list = scores_np.tolist() # Convert back to list + + logger.info(f"New totals: {sum(scores)} scores, {sum(logits)} logits") + + # Get the taxon with the highest score using the index of the max score + top_index = scores.index(max(scores)) + top_taxon = category_map_with_taxa[top_index][ + "taxon" + ] # @TODO: This doesn't work if the taxon has never been classified + print("Top taxon: ", category_map_with_taxa[top_index]) # @TODO: REMOVE + print("Top index: ", top_index) # @TODO: REMOVE + + # check if needs updating + if classification.scores == scores and classification.logits == logits: + logger.debug(f"Classification {classification.pk} does not need updating") + continue + + # Consider the existing classification as an intermediate classification + classification.terminal = False + classification.updated_at = timestamp + + # Recalculate the top taxon and score + new_classification = Classification( + taxon=top_taxon, + algorithm=classification.algorithm, + score=max(scores), + scores=scores, + logits=logits, + detection=classification.detection, + timestamp=classification.timestamp, + terminal=True, + category_map=None, # @TODO need a new category map with the filtered taxa + created_at=timestamp, + updated_at=timestamp, + ) + if new_classification.taxon is None: + raise (ValueError("Classification isn't registered yet. Aborting")) # @TODO remove or fail gracefully + + classifications_to_update.append(classification) + classifications_to_add.append(new_classification) + + assert new_classification.detection is not None + assert new_classification.detection.occurrence is not None + occurrences_to_update.add(new_classification.detection.occurrence) + + logging.info( + f"Adding new classification for Taxon {top_taxon} to occurrence {new_classification.detection.occurrence}" + ) + + # Bulk update the existing classifications + if classifications_to_update: + logger.info(f"Bulk updating {len(classifications_to_update)} existing classifications") + Classification.objects.bulk_update(classifications_to_update, ["terminal", "updated_at"]) + logger.info(f"Updated {len(classifications_to_update)} existing classifications") + + if classifications_to_add: + # Bulk create the new classifications + logger.info(f"Bulk creating {len(classifications_to_add)} new classifications") + Classification.objects.bulk_create(classifications_to_add) + logger.info(f"Added {len(classifications_to_add)} new classifications") + + # Update the occurrence determinations + logger.info(f"Updating the determinations for {len(occurrences_to_update)} occurrences") + for occurrence in occurrences_to_update: + occurrence.save(update_determination=True) + logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") + + +@register_postprocessing_task +class ClassMaskingTask(BasePostProcessingTask): + key = "class_masking" + name = "Class masking" + + def run(self) -> None: + """Apply class masking on a source image collection using a taxa list.""" + job = self.job + self.logger.info(f"=== Starting {self.name} ===") + + collection_id = self.config.get("collection_id") + taxa_list_id = self.config.get("taxa_list_id") + algorithm_id = self.config.get("algorithm_id") + + # Validate config parameters + if not all([collection_id, taxa_list_id, algorithm_id]): + self.logger.error("Missing required configuration: collection_id, taxa_list_id, algorithm_id") + return + + try: + collection = SourceImageCollection.objects.get(pk=collection_id) + taxa_list = TaxaList.objects.get(pk=taxa_list_id) + algorithm = Algorithm.objects.get(pk=algorithm_id) + except Exception as e: + self.logger.exception(f"Failed to load objects: {e}") + return + + self.logger.info(f"Applying class masking on collection {collection_id} using taxa list {taxa_list_id}") + + update_occurrences_in_collection( + collection=collection, + taxa_list=taxa_list, + algorithm=algorithm, + params=self.config, + task_logger=self.logger, + job=job, + ) + + self.logger.info("Class masking completed successfully.") + self.logger.info(f"=== Completed {self.name} ===") diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index 33f9901b9..a8f9b9010 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -31,7 +31,7 @@ def run(self) -> None: try: collection = SourceImageCollection.objects.get(pk=collection_id) - self.logger.info(f"Loaded SourceImageCollection {collection_id} " f"(Project={collection.project})") + self.logger.info(f"Loaded SourceImageCollection {collection_id} (Project={collection.project})") except SourceImageCollection.DoesNotExist: msg = f"SourceImageCollection {collection_id} not found" self.logger.error(msg) @@ -85,6 +85,11 @@ def run(self) -> None: comment=f"Auto-set by {self.name} post-processing task", ) modified += 1 - self.logger.debug(f"Detection {det.pk}: marked as 'Not identifiable'") + self.logger.info(f"Detection {det.pk}: marked as 'Not identifiable'") + + # Update progress every 10 detections + if i % 10 == 0 or i == total: + progress = i / total if total > 0 else 1.0 + self.update_progress(progress) self.logger.info(f"=== Completed {self.name}: {modified}/{total} detections modified ===") From 951960058b48cc12794389a57a018aa12e92fbe1 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 14:55:24 -0400 Subject: [PATCH 29/44] chore: remove class masking trigger (moved to feat/postprocessing-class-masking branch) --- ami/main/admin.py | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index 4519ae6db..5cfa03b79 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -641,32 +641,6 @@ def run_small_size_filter(self, request: HttpRequest, queryset: QuerySet[SourceI self.message_user(request, f"Queued Small Size Filter for {queryset.count()} collection(s). Jobs: {jobs}") - @admin.action(description="Run Class Masking post-processing task (async)") - def run_class_masking(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: - jobs = [] - - DEFAULT_TAXA_LIST_ID = 5 - DEFAULT_ALGORITHM_ID = 11 - - for collection in queryset: - job = Job.objects.create( - name=f"Post-processing: ClassMasking on Collection {collection.pk}", - project=collection.project, - job_type_key="post_processing", - params={ - "task": "class_masking", - "config": { - "collection_id": collection.pk, - "taxa_list_id": DEFAULT_TAXA_LIST_ID, - "algorithm_id": DEFAULT_ALGORITHM_ID, - }, - }, - ) - job.enqueue() - jobs.append(job.pk) - - self.message_user(request, f"Queued Class Masking for {queryset.count()} collection(s). Jobs: {jobs}") - @admin.action(description="Run Rank Rollup post-processing task (async)") def run_rank_rollup(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: """Trigger the Rank Rollup post-processing job asynchronously.""" @@ -692,7 +666,6 @@ def run_rank_rollup(self, request: HttpRequest, queryset: QuerySet[SourceImageCo populate_collection, populate_collection_async, run_small_size_filter, - run_class_masking, run_rank_rollup, ] From 21e664841a2f3e4aff069d02d668241911cc263a Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 15:40:19 -0400 Subject: [PATCH 30/44] feat: improved progress tracking --- ami/ml/post_processing/rank_rollup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index 9f1e3b8f8..70a821e80 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -147,8 +147,8 @@ def run(self) -> None: else: self.logger.info(f"No rollup applied for classification #{clf.pk} (taxon={clf.taxon})") - # 🔹 Periodic progress updates - if i % 50 == 0 or i == total: + # Update progress every 10 iterations + if i % 10 == 0 or i == total: progress = i / total if total > 0 else 1.0 self.update_progress(progress) From 6632c314c98f30020572e6652c3f8ae27aa171fa Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Tue, 14 Oct 2025 21:34:28 -0400 Subject: [PATCH 31/44] feat: add applied_to field to Classification to track source classification --- .../0078_classification_applied_to.py | 25 +++++++++++++++++++ ami/main/models.py | 12 ++++++++- 2 files changed, 36 insertions(+), 1 deletion(-) create mode 100644 ami/main/migrations/0078_classification_applied_to.py diff --git a/ami/main/migrations/0078_classification_applied_to.py b/ami/main/migrations/0078_classification_applied_to.py new file mode 100644 index 000000000..b938f42bb --- /dev/null +++ b/ami/main/migrations/0078_classification_applied_to.py @@ -0,0 +1,25 @@ +# Generated by Django 4.2.10 on 2025-10-14 21:32 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + dependencies = [ + ("main", "0077_merge_20251014_1426"), + ] + + operations = [ + migrations.AddField( + model_name="classification", + name="applied_to", + field=models.ForeignKey( + blank=True, + help_text="If this classification was produced by a post-processing algorithm, this field references the original classification it was applied to.", + null=True, + on_delete=django.db.models.deletion.SET_NULL, + related_name="derived_classifications", + to="main.classification", + ), + ), + ] diff --git a/ami/main/models.py b/ami/main/models.py index 9556018f4..1a9028cb7 100644 --- a/ami/main/models.py +++ b/ami/main/models.py @@ -2226,7 +2226,17 @@ class Classification(BaseModel): related_name="classifications", ) # job = models.CharField(max_length=255, null=True) - + applied_to = models.ForeignKey( + "self", + on_delete=models.SET_NULL, + null=True, + blank=True, + related_name="derived_classifications", + help_text=( + "If this classification was produced by a post-processing algorithm, " + "this field references the original classification it was applied to." + ), + ) objects = ClassificationManager() # Type hints for auto-generated fields From 23f80fb5311fc24d416e0e50f231de8cbd72d22b Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 15 Oct 2025 00:13:11 -0400 Subject: [PATCH 32/44] tests: added tests for small size filter and rank roll up post-processing tasks --- ami/ml/tests.py | 209 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 206 insertions(+), 3 deletions(-) diff --git a/ami/ml/tests.py b/ami/ml/tests.py index d14c04dfb..43e90fac0 100644 --- a/ami/ml/tests.py +++ b/ami/ml/tests.py @@ -1,13 +1,28 @@ import datetime +import pathlib import unittest +import uuid from django.test import TestCase from rest_framework.test import APIRequestFactory, APITestCase from ami.base.serializers import reverse_with_params -from ami.main.models import Detection, Project, SourceImage, SourceImageCollection -from ami.ml.models import Algorithm, Pipeline, ProcessingService +from ami.main.models import ( + Classification, + Detection, + Occurrence, + Project, + SourceImage, + SourceImageCollection, + Taxon, + TaxonRank, + group_images_into_events, +) +from ami.ml.models import Algorithm, AlgorithmCategoryMap, Pipeline, ProcessingService +from ami.ml.models.algorithm import AlgorithmTaskType from ami.ml.models.pipeline import collect_images, get_or_create_algorithm_and_category_map, save_results +from ami.ml.post_processing.rank_rollup import RankRollupTask +from ami.ml.post_processing.small_size_filter import SmallSizeFilterTask from ami.ml.schemas import ( AlgorithmConfigResponse, AlgorithmReference, @@ -17,7 +32,12 @@ PipelineResultsResponse, SourceImageResponse, ) -from ami.tests.fixtures.main import create_captures_from_files, create_processing_service, setup_test_project +from ami.tests.fixtures.main import ( + create_captures_from_files, + create_processing_service, + create_taxa, + setup_test_project, +) from ami.tests.fixtures.ml import ALGORITHM_CHOICES from ami.users.models import User @@ -713,3 +733,186 @@ def test_labels_data_conversion_methods(self): # Verify conversions are correct self.assertEqual(test_data, converted_data) self.assertEqual(test_labels, converted_labels) + + +class TestPostProcessingTasks(TestCase): + def setUp(self): + # Create test project, deployment, and default setup + self.project, self.deployment = setup_test_project() + create_taxa(project=self.project) + self._create_images_with_dimensions(deployment=self.deployment) + group_images_into_events(deployment=self.deployment) + + # Create a simple SourceImageCollection for testing + self.collection = SourceImageCollection.objects.create( + name="Test PostProcessing Collection", + project=self.project, + method="manual", + kwargs={"image_ids": list(self.deployment.captures.values_list("pk", flat=True))}, + ) + self.collection.populate_sample() + + # Select example taxa + self.species_taxon = Taxon.objects.filter(rank=TaxonRank.SPECIES.name).first() + self.genus_taxon = self.species_taxon.parent if self.species_taxon else None + self.assertIsNotNone(self.species_taxon) + self.assertIsNotNone(self.genus_taxon) + self.algorithm = self._create_category_map_with_algorithm() + + def _create_images_with_dimensions( + self, + deployment, + num_images: int = 5, + width: int = 640, + height: int = 480, + update_deployment: bool = True, + ): + """ + Create SourceImages for a deployment with specified width and height. + """ + + created = [] + base_time = datetime.datetime.now(datetime.timezone.utc) + + for i in range(num_images): + random_prefix = uuid.uuid4().hex[:8] + path = pathlib.Path("test") / f"{random_prefix}_{i}.jpg" + + image = SourceImage.objects.create( + deployment=deployment, + project=deployment.project, + timestamp=base_time + datetime.timedelta(minutes=i * 5), + path=path, + width=width, + height=height, + ) + created.append(image) + + if update_deployment: + deployment.save(update_calculated_fields=True, regroup_async=False) + + def test_small_size_filter_assigns_not_identifiable(self): + """ + Test that SmallSizeFilterTask correctly assigns 'Not identifiable' + to detections below the configured minimum size. + """ + # Create small detections on the collection images + for image in self.collection.images.all(): + Detection.objects.create( + source_image=image, + bbox=[0, 0, 10, 10], # small detection + created_at=datetime.datetime.now(datetime.timezone.utc), + ).associate_new_occurrence() + + # Prepare the task configuration + task = SmallSizeFilterTask( + source_image_collection_id=self.collection.pk, + size_threshold=0.00001, + ) + + task.run() + + # Verify that all small detections are now classified as "Not identifiable" + not_identifiable_taxon = Taxon.objects.get(name="Not identifiable") + detections = Detection.objects.filter(source_image__in=self.collection.images.all()) + + for det in detections: + latest_classification = Classification.objects.filter(detection=det).order_by("-created_at").first() + self.assertIsNotNone(latest_classification, "Each detection should have a classification.") + self.assertEqual( + latest_classification.taxon, + not_identifiable_taxon, + f"Detection {det.pk} should be classified as 'Not identifiable'", + ) + + def _create_occurrences_with_classifications(self, num=3): + """Helper to create occurrences and terminal classifications below species threshold.""" + occurrences = [] + now = datetime.datetime.now(datetime.timezone.utc) + for i in range(num): + det = Detection.objects.create( + source_image=self.collection.images.first(), + bbox=[0, 0, 200, 200], + ) + occ = Occurrence.objects.create(project=self.project, event=self.deployment.events.first()) + occ.detections.add(det) + classification = Classification.objects.create( + detection=det, + taxon=self.species_taxon, + score=0.5, + scores=[0.5, 0.3, 0.2], + terminal=True, + timestamp=now, + algorithm=self.algorithm, + ) + occurrences.append((occ, classification)) + return occurrences + + def _create_category_map_with_algorithm(self): + """Create a simple AlgorithmCategoryMap and Algorithm to attach to classifications.""" + species_taxa = list(self.project.taxa.filter(rank=TaxonRank.SPECIES.name)[:3]) + assert species_taxa, "No species taxa found in project; run create_taxa() first." + + data = [ + { + "index": i, + "label": taxon.name, + "taxon_rank": taxon.rank, + "gbif_key": getattr(taxon, "gbif_key", None), + } + for i, taxon in enumerate(species_taxa) + ] + labels = [item["label"] for item in data] + + category_map = AlgorithmCategoryMap.objects.create( + data=data, + labels=labels, + version="v1.0", + description="Species-level category map for testing RankRollupTask", + ) + + algorithm = Algorithm.objects.create( + name="Test Species Classifier", + task_type=AlgorithmTaskType.CLASSIFICATION.value, + category_map=category_map, + ) + + return algorithm + + def test_rank_rollup_creates_new_terminal_classifications(self): + occurrences = self._create_occurrences_with_classifications(num=3) + + task = RankRollupTask( + source_image_collection_id=self.collection.pk, + thresholds={"species": 0.8, "genus": 0.6, "family": 0.4}, + ) + task.run() + + # Validate results + for occ, original_cls in occurrences: + detection = occ.detections.first() + original_cls.refresh_from_db(fields=["terminal"]) + rolled_up_cls = Classification.objects.filter(detection=detection, terminal=True).first() + + self.assertIsNotNone( + rolled_up_cls, + f"Expected a new rolled-up classification for original #{original_cls.pk}", + ) + self.assertTrue( + rolled_up_cls.terminal, + "New rolled-up classification should be marked as terminal.", + ) + self.assertFalse( + original_cls.terminal, + "Original classification should be marked as non-terminal after roll-up.", + ) + self.assertEqual( + rolled_up_cls.taxon, + self.genus_taxon, + "Rolled-up classification should have genus-level taxon.", + ) + self.assertEqual( + rolled_up_cls.applied_to, + original_cls, + "Rolled-up classification should reference the original classification.", + ) From 336636a01de2412bac041d05ace826756f7cb572 Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 15 Oct 2025 00:17:11 -0400 Subject: [PATCH 33/44] fix: create only terminal classifications and remove identification creation --- ami/ml/post_processing/rank_rollup.py | 40 ++++++++++----------- ami/ml/post_processing/small_size_filter.py | 14 ++------ 2 files changed, 22 insertions(+), 32 deletions(-) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index 70a821e80..d23d718b1 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -4,7 +4,7 @@ from django.db import transaction from django.utils import timezone -from ami.main.models import Classification, Identification, Taxon +from ami.main.models import Classification, Taxon from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task logger = logging.getLogger(__name__) @@ -35,8 +35,8 @@ class RankRollupTask(BasePostProcessingTask): key = "rank_rollup" name = "Rank rollup" - DEFAULT_THRESHOLDS = {"species": 0.8, "genus": 0.6, "family": 0.4} - ROLLUP_ORDER = ["species", "genus", "family"] + DEFAULT_THRESHOLDS = {"SPECIES": 0.8, "GENUS": 0.6, "FAMILY": 0.4} + ROLLUP_ORDER = ["SPECIES", "GENUS", "FAMILY"] def run(self) -> None: job = self.job @@ -49,7 +49,7 @@ def run(self) -> None: rollup_order = config.get("rollup_order", self.ROLLUP_ORDER) if not collection_id: - self.logger.warning("No 'source_image_collection_id' provided in config. Aborting task.") + self.logger.info("No 'source_image_collection_id' provided in config. Aborting task.") return self.logger.info( @@ -72,10 +72,10 @@ def run(self) -> None: self.logger.info(f"Processing classification #{clf.pk} (taxon={clf.taxon}, score={clf.score:.3f})") if not clf.scores: - self.logger.warning(f"Skipping classification #{clf.pk}: no scores available") + self.logger.info(f"Skipping classification #{clf.pk}: no scores available") continue if not clf.category_map: - self.logger.warning(f"Skipping classification #{clf.pk}: no category_map assigned") + self.logger.info(f"Skipping classification #{clf.pk}: no category_map assigned") continue taxon_scores = defaultdict(float) @@ -87,7 +87,7 @@ def run(self) -> None: taxon = Taxon.objects.filter(name=label).first() if not taxon: - self.logger.debug(f"Skipping label '{label}' (no matching Taxon in DB)") + self.logger.info(f"Skipping label '{label}' (no matching Taxon Found)") continue for rank in rollup_order: @@ -98,16 +98,20 @@ def run(self) -> None: new_taxon = None new_score = None + self.logger.info(f"Aggregated taxon scores: { {t.name: s for t, s in taxon_scores.items()} }") for rank in rollup_order: threshold = thresholds.get(rank, 1.0) + # import pdb + + # pdb.set_trace() candidates = {t: s for t, s in taxon_scores.items() if t.rank == rank} if not candidates: - self.logger.debug(f"No candidates found at rank {rank}") + self.logger.info(f"No candidates found at rank {rank}") continue best_taxon, best_score = max(candidates.items(), key=lambda kv: kv[1]) - self.logger.debug( + self.logger.info( f"Best at rank {rank}: {best_taxon.name} ({best_score:.3f}) [threshold={threshold}]" ) @@ -117,10 +121,11 @@ def run(self) -> None: break if new_taxon and new_taxon != clf.taxon: - self.logger.info(f"Rolling up {clf.taxon} → {new_taxon} ({new_taxon.rank})") + self.logger.info(f"Rolling up {clf.taxon} => {new_taxon} ({new_taxon.rank})") with transaction.atomic(): - Classification.objects.filter(detection=clf.detection, terminal=True).update(terminal=False) + # Mark all classifications for this detection as non-terminal + Classification.objects.filter(detection=clf.detection).update(terminal=False) Classification.objects.create( detection=clf.detection, taxon=new_taxon, @@ -128,20 +133,13 @@ def run(self) -> None: terminal=True, algorithm=self.algorithm, timestamp=timezone.now(), + applied_to=clf, ) occurrence = clf.detection.occurrence - if occurrence: - Identification.objects.create( - occurrence=occurrence, - taxon=new_taxon, - user=None, - comment=f"Auto-set by {self.name} post-processing task", - ) - updated_occurrences.append(occurrence.pk) - + updated_occurrences.append(occurrence) self.logger.info( - f"Rolled up occurrence {occurrence.pk}: {clf.taxon} → {new_taxon} " + f"Rolled up occurrence {occurrence.pk}: {clf.taxon} => {new_taxon} " f"({new_taxon.rank}) with rolled-up score={new_score:.3f}" ) else: diff --git a/ami/ml/post_processing/small_size_filter.py b/ami/ml/post_processing/small_size_filter.py index a8f9b9010..a21dbf649 100644 --- a/ami/ml/post_processing/small_size_filter.py +++ b/ami/ml/post_processing/small_size_filter.py @@ -1,12 +1,12 @@ from django.db import transaction from django.utils import timezone -from ami.main.models import Detection, Identification, SourceImageCollection, Taxon, TaxonRank +from ami.main.models import Detection, SourceImageCollection, Taxon, TaxonRank from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task @register_postprocessing_task -class SmallSizeFilter(BasePostProcessingTask): +class SmallSizeFilterTask(BasePostProcessingTask): key = "small_size_filter" name = "Small size filter" @@ -58,7 +58,7 @@ def run(self) -> None: img_area = img_w * img_h rel_area = det_area / img_area if img_area else 0 - self.logger.debug( + self.logger.info( f"Detection {det.pk}: area={det_area}, rel_area={rel_area:.4f}, " f"threshold={threshold:.4f}" ) @@ -76,14 +76,6 @@ def run(self) -> None: timestamp=timezone.now(), algorithm=self.algorithm, ) - # Also create/update Identification for the linked occurrence - if det.occurrence: - Identification.objects.create( - occurrence=det.occurrence, - taxon=not_identifiable_taxon, - user=None, # auto-generated by post-processing - comment=f"Auto-set by {self.name} post-processing task", - ) modified += 1 self.logger.info(f"Detection {det.pk}: marked as 'Not identifiable'") From 0d90cdef08d88887a49a2d65481d364a444195bb Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 15 Oct 2025 00:23:52 -0400 Subject: [PATCH 34/44] refactor: remove inner transaction.atomic for cleaner transaction management --- ami/ml/post_processing/rank_rollup.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index d23d718b1..da5177b4e 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -123,18 +123,17 @@ def run(self) -> None: if new_taxon and new_taxon != clf.taxon: self.logger.info(f"Rolling up {clf.taxon} => {new_taxon} ({new_taxon.rank})") - with transaction.atomic(): - # Mark all classifications for this detection as non-terminal - Classification.objects.filter(detection=clf.detection).update(terminal=False) - Classification.objects.create( - detection=clf.detection, - taxon=new_taxon, - score=new_score, - terminal=True, - algorithm=self.algorithm, - timestamp=timezone.now(), - applied_to=clf, - ) + # Mark all classifications for this detection as non-terminal + Classification.objects.filter(detection=clf.detection).update(terminal=False) + Classification.objects.create( + detection=clf.detection, + taxon=new_taxon, + score=new_score, + terminal=True, + algorithm=self.algorithm, + timestamp=timezone.now(), + applied_to=clf, + ) occurrence = clf.detection.occurrence updated_occurrences.append(occurrence) From 23469e261b50fff8e76fdf6edff5a8c9a709643f Mon Sep 17 00:00:00 2001 From: mohamedelabbas1996 Date: Wed, 15 Oct 2025 00:27:28 -0400 Subject: [PATCH 35/44] tests: fixed small size filter test --- ami/ml/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ami/ml/tests.py b/ami/ml/tests.py index 43e90fac0..6340f7df8 100644 --- a/ami/ml/tests.py +++ b/ami/ml/tests.py @@ -807,7 +807,7 @@ def test_small_size_filter_assigns_not_identifiable(self): # Prepare the task configuration task = SmallSizeFilterTask( source_image_collection_id=self.collection.pk, - size_threshold=0.00001, + size_threshold=0.01, ) task.run() From 1b8700ee2d44a3ddab8b5886d1573cc96410d951 Mon Sep 17 00:00:00 2001 From: Michael Bunsen Date: Thu, 16 Oct 2025 01:36:53 -0700 Subject: [PATCH 36/44] draft: work towards class masking in new framework --- ami/main/admin.py | 75 ++++++++++++++++++++++++- ami/ml/post_processing/class_masking.py | 26 +++++++-- ami/ml/post_processing/registry.py | 2 + 3 files changed, 98 insertions(+), 5 deletions(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index ab4b7a698..301293957 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -5,13 +5,17 @@ from django.db.models.query import QuerySet from django.http.request import HttpRequest from django.template.defaultfilters import filesizeformat +from django.urls import reverse from django.utils.formats import number_format +from django.utils.html import format_html from guardian.admin import GuardedModelAdmin import ami.utils from ami import tasks from ami.jobs.models import Job +from ami.ml.models.algorithm import Algorithm from ami.ml.models.project_pipeline_config import ProjectPipelineConfig +from ami.ml.post_processing.class_masking import update_single_occurrence from ami.ml.tasks import remove_duplicate_classifications from .models import ( @@ -289,6 +293,7 @@ class ClassificationInline(admin.TabularInline): model = Classification extra = 0 fields = ( + "classification_link", "taxon", "algorithm", "timestamp", @@ -296,6 +301,7 @@ class ClassificationInline(admin.TabularInline): "created_at", ) readonly_fields = ( + "classification_link", "taxon", "algorithm", "timestamp", @@ -303,6 +309,13 @@ class ClassificationInline(admin.TabularInline): "created_at", ) + @admin.display(description="Classification") + def classification_link(self, obj: Classification) -> str: + if obj.pk: + url = reverse("admin:main_classification_change", args=[obj.pk]) + return format_html('{}', url, f"Classification #{obj.pk}") + return "-" + def get_queryset(self, request: HttpRequest) -> QuerySet[Any]: qs = super().get_queryset(request) return qs.select_related("taxon", "algorithm", "detection") @@ -312,6 +325,7 @@ class DetectionInline(admin.TabularInline): model = Detection extra = 0 fields = ( + "detection_link", "detection_algorithm", "source_image", "timestamp", @@ -319,6 +333,7 @@ class DetectionInline(admin.TabularInline): "occurrence", ) readonly_fields = ( + "detection_link", "detection_algorithm", "source_image", "timestamp", @@ -326,6 +341,13 @@ class DetectionInline(admin.TabularInline): "occurrence", ) + @admin.display(description="ID") + def detection_link(self, obj): + if obj.pk: + url = reverse("admin:main_detection_change", args=[obj.pk]) + return format_html('{}', url, obj.pk) + return "-" + @admin.register(Detection) class DetectionAdmin(admin.ModelAdmin[Detection]): @@ -383,7 +405,7 @@ class OccurrenceAdmin(admin.ModelAdmin[Occurrence]): "determination__rank", "created_at", ) - search_fields = ("determination__name", "determination__search_names") + search_fields = ("id", "determination__name", "determination__search_names") def get_queryset(self, request: HttpRequest) -> QuerySet[Any]: qs = super().get_queryset(request) @@ -405,11 +427,60 @@ def get_queryset(self, request: HttpRequest) -> QuerySet[Any]: def detections_count(self, obj) -> int: return obj.detections_count + @admin.action(description="Update occurrence with Newfoundland species taxa list") + def update_with_newfoundland_species(self, request: HttpRequest, queryset: QuerySet[Occurrence]) -> None: + """ + Update selected occurrences using the 'Newfoundland species' taxa list + and 'Quebec & Vermont Species Classifier - Apr 2024' algorithm. + """ + try: + # Get the taxa list by name + taxa_list = TaxaList.objects.get(name="Newfoundland Species") + except TaxaList.DoesNotExist: + self.message_user( + request, + "Error: TaxaList 'Newfoundland species' not found.", + level="error", + ) + return + + try: + # Get the algorithm by name + algorithm = Algorithm.objects.get(name="Quebec & Vermont Species Classifier - Apr 2024") + except Algorithm.DoesNotExist: + self.message_user( + request, + "Error: Algorithm 'Quebec & Vermont Species Classifier - Apr 2024' not found.", + level="error", + ) + return + + # Process each occurrence + count = 0 + for occurrence in queryset: + try: + update_single_occurrence( + occurrence=occurrence, + algorithm=algorithm, + taxa_list=taxa_list, + ) + count += 1 + except Exception as e: + self.message_user( + request, + f"Error processing occurrence {occurrence.pk}: {str(e)}", + level="error", + ) + + self.message_user(request, f"Successfully updated {count} occurrence(s).") + ordering = ("-created_at",) # Add classifications as inline inlines = [DetectionInline] + actions = [update_with_newfoundland_species] + @admin.register(Classification) class ClassificationAdmin(admin.ModelAdmin[Classification]): @@ -433,6 +504,8 @@ class ClassificationAdmin(admin.ModelAdmin[Classification]): "taxon__rank", ) + autocomplete_fields = ("taxon",) + def get_queryset(self, request: HttpRequest) -> QuerySet[Any]: qs = super().get_queryset(request) return qs.select_related( diff --git a/ami/ml/post_processing/class_masking.py b/ami/ml/post_processing/class_masking.py index 04c621830..298688ba7 100644 --- a/ami/ml/post_processing/class_masking.py +++ b/ami/ml/post_processing/class_masking.py @@ -4,8 +4,8 @@ from django.utils import timezone from ami.main.models import Classification, Occurrence, SourceImageCollection, TaxaList -from ami.ml.models import Algorithm, AlgorithmCategoryMap -from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task +from ami.ml.models.algorithm import Algorithm, AlgorithmCategoryMap, AlgorithmTaskType +from ami.ml.post_processing.base import BasePostProcessingTask logger = logging.getLogger(__name__) @@ -26,10 +26,22 @@ def update_single_occurrence( scores__isnull=False, ).distinct() + # Make a new Algorithm for the filtered classifications + new_algorithm, _ = Algorithm.objects.get_or_create( + name=f"{algorithm.name} (filtered by taxa list {taxa_list.name})", + key=f"{algorithm.key}_filtered_by_taxa_list_{taxa_list.pk}", + defaults={ + "description": f"Classification algorithm {algorithm.name} filtered by taxa list {taxa_list.name}", + "task_type": AlgorithmTaskType.CLASSIFICATION.value, + "category_map": algorithm.category_map, + }, + ) + make_classifications_filtered_by_taxa_list( classifications=classifications, taxa_list=taxa_list, algorithm=algorithm, + new_algorithm=new_algorithm, ) @@ -38,6 +50,7 @@ def update_occurrences_in_collection( taxa_list: TaxaList, algorithm: Algorithm, params: dict, + new_algorithm: Algorithm, task_logger: logging.Logger = logger, job=None, ): @@ -58,6 +71,7 @@ def update_occurrences_in_collection( classifications=classifications, taxa_list=taxa_list, algorithm=algorithm, + new_algorithm=new_algorithm, ) @@ -65,6 +79,7 @@ def make_classifications_filtered_by_taxa_list( classifications: QuerySet[Classification], taxa_list: TaxaList, algorithm: Algorithm, + new_algorithm: Algorithm, ): taxa_in_list = taxa_list.taxa.all() @@ -167,7 +182,7 @@ def make_classifications_filtered_by_taxa_list( # Recalculate the top taxon and score new_classification = Classification( taxon=top_taxon, - algorithm=classification.algorithm, + algorithm=new_algorithm, score=max(scores), scores=scores, logits=logits, @@ -211,7 +226,6 @@ def make_classifications_filtered_by_taxa_list( logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") -@register_postprocessing_task class ClassMaskingTask(BasePostProcessingTask): key = "class_masking" name = "Class masking" @@ -240,6 +254,9 @@ def run(self) -> None: self.logger.info(f"Applying class masking on collection {collection_id} using taxa list {taxa_list_id}") + # @TODO temporary, do we need a new algorithm for each class mask? + self.algorithm.category_map = algorithm.category_map # Ensure the algorithm has its category map loaded + update_occurrences_in_collection( collection=collection, taxa_list=taxa_list, @@ -247,6 +264,7 @@ def run(self) -> None: params=self.config, task_logger=self.logger, job=job, + new_algorithm=self.algorithm, ) self.logger.info("Class masking completed successfully.") diff --git a/ami/ml/post_processing/registry.py b/ami/ml/post_processing/registry.py index c85f607f9..308be18ae 100644 --- a/ami/ml/post_processing/registry.py +++ b/ami/ml/post_processing/registry.py @@ -1,8 +1,10 @@ # Registry of available post-processing tasks +from ami.ml.post_processing.class_masking import ClassMaskingTask from ami.ml.post_processing.small_size_filter import SmallSizeFilterTask POSTPROCESSING_TASKS = { SmallSizeFilterTask.key: SmallSizeFilterTask, + ClassMaskingTask.key: ClassMaskingTask, } From a466a524230301318a93eaed376b08183a3be603 Mon Sep 17 00:00:00 2001 From: Michael Bunsen Date: Wed, 18 Feb 2026 03:44:51 -0800 Subject: [PATCH 37/44] feat: add class masking tests, management command, and fix registry - Add 4 class masking tests: score redistribution, accuracy improvement, no-op when all species in list, and softmax mathematical correctness - Add `run_class_masking` management command with --collection-id, --taxa-list-id, --algorithm-id, and --dry-run options - Register RankRollupTask in post-processing registry - Remove non-existent `register_postprocessing_task` import/decorator from rank_rollup.py Co-Authored-By: Claude --- .../management/commands/run_class_masking.py | 83 +++++ ami/ml/post_processing/rank_rollup.py | 6 +- ami/ml/post_processing/registry.py | 2 + ami/ml/tests.py | 317 ++++++++++++++++++ 4 files changed, 405 insertions(+), 3 deletions(-) create mode 100644 ami/ml/management/commands/run_class_masking.py diff --git a/ami/ml/management/commands/run_class_masking.py b/ami/ml/management/commands/run_class_masking.py new file mode 100644 index 000000000..d87375d74 --- /dev/null +++ b/ami/ml/management/commands/run_class_masking.py @@ -0,0 +1,83 @@ +from django.core.management.base import BaseCommand, CommandError + +from ami.main.models import SourceImageCollection, TaxaList +from ami.ml.models.algorithm import Algorithm +from ami.ml.post_processing.class_masking import ClassMaskingTask + + +class Command(BaseCommand): + help = ( + "Run class masking post-processing on a source image collection. " + "Masks classifier logits for species not in the given taxa list and recalculates softmax scores." + ) + + def add_arguments(self, parser): + parser.add_argument("--collection-id", type=int, required=True, help="SourceImageCollection ID to process") + parser.add_argument("--taxa-list-id", type=int, required=True, help="TaxaList ID to use as the species mask") + parser.add_argument( + "--algorithm-id", type=int, required=True, help="Algorithm ID whose classifications to mask" + ) + parser.add_argument("--dry-run", action="store_true", help="Show what would be done without making changes") + + def handle(self, *args, **options): + collection_id = options["collection_id"] + taxa_list_id = options["taxa_list_id"] + algorithm_id = options["algorithm_id"] + dry_run = options["dry_run"] + + # Validate inputs + try: + collection = SourceImageCollection.objects.get(pk=collection_id) + except SourceImageCollection.DoesNotExist: + raise CommandError(f"SourceImageCollection {collection_id} does not exist.") + + try: + taxa_list = TaxaList.objects.get(pk=taxa_list_id) + except TaxaList.DoesNotExist: + raise CommandError(f"TaxaList {taxa_list_id} does not exist.") + + try: + algorithm = Algorithm.objects.get(pk=algorithm_id) + except Algorithm.DoesNotExist: + raise CommandError(f"Algorithm {algorithm_id} does not exist.") + + if not algorithm.category_map: + raise CommandError(f"Algorithm '{algorithm.name}' does not have a category map.") + + from ami.main.models import Classification + + classification_count = ( + Classification.objects.filter( + detection__source_image__collections=collection, + terminal=True, + algorithm=algorithm, + scores__isnull=False, + ) + .distinct() + .count() + ) + + taxa_count = taxa_list.taxa.count() + + self.stdout.write( + f"Collection: {collection.name} (id={collection.pk})\n" + f"Taxa list: {taxa_list.name} (id={taxa_list.pk}, {taxa_count} taxa)\n" + f"Algorithm: {algorithm.name} (id={algorithm.pk})\n" + f"Classifications to process: {classification_count}" + ) + + if classification_count == 0: + raise CommandError("No terminal classifications with scores found for this collection/algorithm.") + + if dry_run: + self.stdout.write(self.style.WARNING("Dry run — no changes made.")) + return + + self.stdout.write("Running class masking...") + task = ClassMaskingTask( + collection_id=collection_id, + taxa_list_id=taxa_list_id, + algorithm_id=algorithm_id, + ) + task.run() + self.stdout.write(self.style.SUCCESS("Class masking completed.")) diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index da5177b4e..9708b1c63 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -5,7 +5,7 @@ from django.utils import timezone from ami.main.models import Classification, Taxon -from ami.ml.post_processing.base import BasePostProcessingTask, register_postprocessing_task +from ami.ml.post_processing.base import BasePostProcessingTask logger = logging.getLogger(__name__) @@ -26,7 +26,6 @@ def find_ancestor_by_parent_chain(taxon, target_rank: str): return None -@register_postprocessing_task class RankRollupTask(BasePostProcessingTask): """Post-processing task that rolls up low-confidence classifications to higher ranks using aggregated scores. @@ -98,7 +97,8 @@ def run(self) -> None: new_taxon = None new_score = None - self.logger.info(f"Aggregated taxon scores: { {t.name: s for t, s in taxon_scores.items()} }") + scores_str = {t.name: s for t, s in taxon_scores.items()} + self.logger.info(f"Aggregated taxon scores: {scores_str}") for rank in rollup_order: threshold = thresholds.get(rank, 1.0) # import pdb diff --git a/ami/ml/post_processing/registry.py b/ami/ml/post_processing/registry.py index 308be18ae..28fa7fb2f 100644 --- a/ami/ml/post_processing/registry.py +++ b/ami/ml/post_processing/registry.py @@ -1,10 +1,12 @@ # Registry of available post-processing tasks from ami.ml.post_processing.class_masking import ClassMaskingTask +from ami.ml.post_processing.rank_rollup import RankRollupTask from ami.ml.post_processing.small_size_filter import SmallSizeFilterTask POSTPROCESSING_TASKS = { SmallSizeFilterTask.key: SmallSizeFilterTask, ClassMaskingTask.key: ClassMaskingTask, + RankRollupTask.key: RankRollupTask, } diff --git a/ami/ml/tests.py b/ami/ml/tests.py index 99c6c5a39..aecf8859f 100644 --- a/ami/ml/tests.py +++ b/ami/ml/tests.py @@ -14,6 +14,7 @@ Project, SourceImage, SourceImageCollection, + TaxaList, Taxon, TaxonRank, group_images_into_events, @@ -21,6 +22,7 @@ from ami.ml.models import Algorithm, AlgorithmCategoryMap, Pipeline, ProcessingService from ami.ml.models.algorithm import AlgorithmTaskType from ami.ml.models.pipeline import collect_images, get_or_create_algorithm_and_category_map, save_results +from ami.ml.post_processing.class_masking import make_classifications_filtered_by_taxa_list from ami.ml.post_processing.rank_rollup import RankRollupTask from ami.ml.post_processing.small_size_filter import SmallSizeFilterTask from ami.ml.schemas import ( @@ -1006,6 +1008,321 @@ def test_rank_rollup_creates_new_terminal_classifications(self): "Rolled-up classification should reference the original classification.", ) + def _create_classification_with_logits(self, detection, taxon, score, scores, logits): + """Helper to create a classification with explicit scores and logits.""" + now = datetime.datetime.now(datetime.timezone.utc) + return Classification.objects.create( + detection=detection, + taxon=taxon, + score=score, + scores=scores, + logits=logits, + terminal=True, + timestamp=now, + algorithm=self.algorithm, + ) + + def test_class_masking_redistributes_scores(self): + """ + Test that class masking correctly recalculates softmax after masking excluded species. + + Setup: 3 species in category map (indices 0, 1, 2). + Taxa list contains only species at indices 0 and 1. + Original classification has species at index 2 as the top prediction. + After masking, the top prediction should shift to species 0 or 1. + """ + import math + + species_taxa = list(self.project.taxa.filter(rank=TaxonRank.SPECIES.name).order_by("name")[:3]) + self.assertEqual(len(species_taxa), 3) + + # Create a taxa list with only the first 2 species (exclude species_taxa[2]) + partial_taxa_list = TaxaList.objects.create(name="Partial Species List") + partial_taxa_list.taxa.set(species_taxa[:2]) + + # Logits where excluded species (index 2) has the highest value + logits = [2.0, 1.0, 5.0] # species[2] dominates + # Compute original softmax + max_logit = max(logits) + exp_logits = [math.exp(x - max_logit) for x in logits] + total = sum(exp_logits) + original_scores = [e / total for e in exp_logits] + + # Original top prediction is species[2] (the excluded one) + self.assertEqual(original_scores.index(max(original_scores)), 2) + + det = Detection.objects.create( + source_image=self.collection.images.first(), + bbox=[0, 0, 200, 200], + ) + occ = Occurrence.objects.create(project=self.project, event=self.deployment.events.first()) + occ.detections.add(det) + + original_clf = self._create_classification_with_logits( + detection=det, + taxon=species_taxa[2], # top prediction is the excluded species + score=max(original_scores), + scores=original_scores, + logits=logits, + ) + + # Create a new algorithm for masked output + new_algorithm, _ = Algorithm.objects.get_or_create( + name=f"{self.algorithm.name} (filtered by {partial_taxa_list.name})", + key=f"{self.algorithm.key}_filtered_{partial_taxa_list.pk}", + defaults={ + "task_type": AlgorithmTaskType.CLASSIFICATION.value, + "category_map": self.algorithm.category_map, + }, + ) + + classifications = Classification.objects.filter(pk=original_clf.pk) + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=partial_taxa_list, + algorithm=self.algorithm, + new_algorithm=new_algorithm, + ) + + # Original classification should be non-terminal + original_clf.refresh_from_db() + self.assertFalse(original_clf.terminal, "Original classification should be non-terminal after masking.") + + # New terminal classification should exist + new_clf = Classification.objects.filter(detection=det, terminal=True).first() + self.assertIsNotNone(new_clf, "A new terminal classification should be created.") + self.assertEqual(new_clf.algorithm, new_algorithm) + + # New top prediction should be species[0] (highest logit among allowed species) + self.assertEqual( + new_clf.taxon, + species_taxa[0], + "Top prediction should be the highest-scoring species remaining in the taxa list.", + ) + + # Scores should sum to ~1.0 (valid probability distribution) + self.assertAlmostEqual(sum(new_clf.scores), 1.0, places=5, msg="Masked scores should sum to 1.0") + + # Excluded species score should be ~0.0 + self.assertAlmostEqual( + new_clf.scores[2], + 0.0, + places=10, + msg="Excluded species score should be effectively zero.", + ) + + # New top score should be higher than original (probability mass redistributed) + self.assertGreater( + new_clf.score, + original_scores[0], + "In-list species score should increase after masking out the dominant excluded species.", + ) + + def test_class_masking_improves_accuracy(self): + """ + Test the key use case: class masking improves accuracy when the true species is in + the taxa list but was originally outscored by an out-of-list species. + + Scenario: True species is "Vanessa cardui" (in list). The classifier's top prediction + is an out-of-list species. After masking, "Vanessa cardui" should become the top + prediction, and the occurrence determination should update. + """ + species_taxa = list(self.project.taxa.filter(rank=TaxonRank.SPECIES.name).order_by("name")[:3]) + self.assertEqual(len(species_taxa), 3) + # species_taxa sorted by name: [Vanessa atalanta, Vanessa cardui, Vanessa itea] + + true_species = species_taxa[1] # Vanessa cardui — the "ground truth" + excluded_species = species_taxa[2] # Vanessa itea — not in the regional list + + # Taxa list: contains atalanta and cardui, but NOT itea + regional_list = TaxaList.objects.create(name="Regional Species List") + regional_list.taxa.set([species_taxa[0], species_taxa[1]]) + + # Logits: itea (index 2) is top, cardui (index 1) is close second, atalanta (index 0) is low + logits = [0.5, 3.0, 3.5] + + import math + + max_logit = max(logits) + exp_logits = [math.exp(x - max_logit) for x in logits] + total = sum(exp_logits) + scores = [e / total for e in exp_logits] + + # Original top prediction is the excluded species + self.assertEqual(scores.index(max(scores)), 2) + + det = Detection.objects.create( + source_image=self.collection.images.first(), + bbox=[0, 0, 200, 200], + ) + occ = Occurrence.objects.create(project=self.project, event=self.deployment.events.first()) + occ.detections.add(det) + + self._create_classification_with_logits( + detection=det, + taxon=excluded_species, + score=max(scores), + scores=scores, + logits=logits, + ) + # Occurrence determination is currently the excluded species + occ.save(update_determination=True) + occ.refresh_from_db() + self.assertEqual(occ.determination, excluded_species) + + new_algorithm, _ = Algorithm.objects.get_or_create( + name=f"{self.algorithm.name} (filtered by {regional_list.name})", + key=f"{self.algorithm.key}_filtered_{regional_list.pk}", + defaults={ + "task_type": AlgorithmTaskType.CLASSIFICATION.value, + "category_map": self.algorithm.category_map, + }, + ) + + classifications = Classification.objects.filter( + detection__occurrence=occ, + terminal=True, + algorithm=self.algorithm, + scores__isnull=False, + ) + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=regional_list, + algorithm=self.algorithm, + new_algorithm=new_algorithm, + ) + + # After masking, occurrence determination should be the true species + occ.refresh_from_db() + self.assertEqual( + occ.determination, + true_species, + "After class masking, occurrence determination should update to the correct in-list species.", + ) + + # Verify the new classification's taxon + new_clf = Classification.objects.filter(detection=det, terminal=True).first() + self.assertEqual(new_clf.taxon, true_species) + self.assertGreater(new_clf.score, 0.5, "Masked score for true species should be > 0.5") + + def test_class_masking_no_change_when_all_species_in_list(self): + """When all category map species are in the taxa list, no new classifications should be created.""" + species_taxa = list(self.project.taxa.filter(rank=TaxonRank.SPECIES.name).order_by("name")[:3]) + + # Taxa list contains ALL species + full_list = TaxaList.objects.create(name="Full Species List") + full_list.taxa.set(species_taxa) + + logits = [3.0, 1.0, 0.5] + import math + + max_logit = max(logits) + exp_logits = [math.exp(x - max_logit) for x in logits] + total = sum(exp_logits) + scores = [e / total for e in exp_logits] + + det = Detection.objects.create( + source_image=self.collection.images.first(), + bbox=[0, 0, 200, 200], + ) + occ = Occurrence.objects.create(project=self.project, event=self.deployment.events.first()) + occ.detections.add(det) + + original_clf = self._create_classification_with_logits( + detection=det, + taxon=species_taxa[0], + score=max(scores), + scores=scores, + logits=logits, + ) + + new_algorithm, _ = Algorithm.objects.get_or_create( + name=f"{self.algorithm.name} (filtered full)", + key=f"{self.algorithm.key}_filtered_full", + defaults={ + "task_type": AlgorithmTaskType.CLASSIFICATION.value, + "category_map": self.algorithm.category_map, + }, + ) + + classifications = Classification.objects.filter(pk=original_clf.pk) + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=full_list, + algorithm=self.algorithm, + new_algorithm=new_algorithm, + ) + + # Original should still be terminal (no change needed) + original_clf.refresh_from_db() + self.assertTrue(original_clf.terminal, "Original should remain terminal when all species are in the list.") + + # No new classifications created + clf_count = Classification.objects.filter(detection=det).count() + self.assertEqual(clf_count, 1, "No new classification should be created when masking changes nothing.") + + def test_class_masking_softmax_correctness(self): + """Verify that masked softmax produces mathematically correct results.""" + import math + + species_taxa = list(self.project.taxa.filter(rank=TaxonRank.SPECIES.name).order_by("name")[:3]) + + # Only keep species at index 0 + single_species_list = TaxaList.objects.create(name="Single Species List") + single_species_list.taxa.set([species_taxa[0]]) + + logits = [2.0, 3.0, 4.0] + max_logit = max(logits) + exp_logits = [math.exp(x - max_logit) for x in logits] + total = sum(exp_logits) + scores = [e / total for e in exp_logits] + + det = Detection.objects.create( + source_image=self.collection.images.first(), + bbox=[0, 0, 200, 200], + ) + occ = Occurrence.objects.create(project=self.project, event=self.deployment.events.first()) + occ.detections.add(det) + + self._create_classification_with_logits( + detection=det, + taxon=species_taxa[2], # original top is index 2 + score=max(scores), + scores=scores, + logits=logits, + ) + + new_algorithm, _ = Algorithm.objects.get_or_create( + name=f"{self.algorithm.name} (single species)", + key=f"{self.algorithm.key}_single", + defaults={ + "task_type": AlgorithmTaskType.CLASSIFICATION.value, + "category_map": self.algorithm.category_map, + }, + ) + + classifications = Classification.objects.filter(detection=det, terminal=True) + make_classifications_filtered_by_taxa_list( + classifications=classifications, + taxa_list=single_species_list, + algorithm=self.algorithm, + new_algorithm=new_algorithm, + ) + + new_clf = Classification.objects.filter(detection=det, terminal=True).first() + self.assertIsNotNone(new_clf) + + # With only 1 allowed species, its score should be ~1.0 + self.assertAlmostEqual( + new_clf.scores[0], + 1.0, + places=5, + msg="With only one allowed species, its softmax score should be ~1.0", + ) + self.assertAlmostEqual(new_clf.scores[1], 0.0, places=10) + self.assertAlmostEqual(new_clf.scores[2], 0.0, places=10) + self.assertAlmostEqual(sum(new_clf.scores), 1.0, places=5) + class TestTaskStateManager(TestCase): """Test TaskStateManager for job progress tracking.""" From a1075979f28433afddecc16717b85a5acdb128be Mon Sep 17 00:00:00 2001 From: Michael Bunsen Date: Wed, 18 Feb 2026 03:59:08 -0800 Subject: [PATCH 38/44] fix: address review feedback on class masking and rank rollup - Move numpy imports to module level (class_masking.py) - Replace print() with logger.debug() - Use .count() instead of len() for QuerySet evaluation - Convert taxa_in_list to set() for O(1) lookups - Add logits__isnull=False to queryset filters - Set category_map on new classifications (was None) - Fix error message clarity and remove extra parens - Use logger.info() instead of root logging.info() - Wrap bulk_update/bulk_create in transaction.atomic() - Normalize threshold keys to uppercase in RankRollupTask - Guard clf.score formatting against None - Guard against null occurrence in rank rollup - Remove commented-out pdb lines - Fix admin.py threshold keys to uppercase - Fix test scores to exercise intended thresholds - Clean up stale TODO comments and dead code Co-Authored-By: Claude --- ami/main/admin.py | 2 +- ami/ml/post_processing/class_masking.py | 92 ++++++++++--------------- ami/ml/post_processing/rank_rollup.py | 22 +++--- ami/ml/tests.py | 4 +- 4 files changed, 52 insertions(+), 68 deletions(-) diff --git a/ami/main/admin.py b/ami/main/admin.py index 95437b29b..4d7d76c09 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -739,7 +739,7 @@ def run_small_size_filter(self, request: HttpRequest, queryset: QuerySet[SourceI def run_rank_rollup(self, request: HttpRequest, queryset: QuerySet[SourceImageCollection]) -> None: """Trigger the Rank Rollup post-processing job asynchronously.""" jobs = [] - DEFAULT_THRESHOLDS = {"species": 0.8, "genus": 0.6, "family": 0.4} + DEFAULT_THRESHOLDS = {"SPECIES": 0.8, "GENUS": 0.6, "FAMILY": 0.4} for collection in queryset: job = Job.objects.create( diff --git a/ami/ml/post_processing/class_masking.py b/ami/ml/post_processing/class_masking.py index 298688ba7..731776db3 100644 --- a/ami/ml/post_processing/class_masking.py +++ b/ami/ml/post_processing/class_masking.py @@ -1,5 +1,7 @@ import logging +import numpy as np +from django.db import transaction from django.db.models import QuerySet from django.utils import timezone @@ -24,6 +26,7 @@ def update_single_occurrence( terminal=True, algorithm=algorithm, scores__isnull=False, + logits__isnull=False, ).distinct() # Make a new Algorithm for the filtered classifications @@ -56,15 +59,12 @@ def update_occurrences_in_collection( ): task_logger.info(f"Recalculating classifications based on a taxa list. Params: {params}") - # Make new AlgorithmCategoryMap with the taxa in the list - # @TODO - classifications = Classification.objects.filter( detection__source_image__collections=collection, terminal=True, - # algorithm__task_type="classification", algorithm=algorithm, scores__isnull=False, + logits__isnull=False, ).distinct() make_classifications_filtered_by_taxa_list( @@ -81,32 +81,26 @@ def make_classifications_filtered_by_taxa_list( algorithm: Algorithm, new_algorithm: Algorithm, ): - taxa_in_list = taxa_list.taxa.all() + taxa_in_list = set(taxa_list.taxa.all()) occurrences_to_update: set[Occurrence] = set() - logger.info(f"Found {len(classifications)} terminal classifications with scores to update.") + classification_count = classifications.count() + logger.info(f"Found {classification_count} terminal classifications with scores to update.") - if not classifications: + if classification_count == 0: raise ValueError("No terminal classifications with scores found to update.") if not algorithm.category_map: raise ValueError(f"Algorithm {algorithm} does not have a category map.") category_map: AlgorithmCategoryMap = algorithm.category_map - # Consider moving this to a method on the Classification model - # @TODO find a more efficient way to get the category map with taxa. This is slow! logger.info(f"Retrieving category map with Taxa instances for algorithm {algorithm}") category_map_with_taxa = category_map.with_taxa() - # Filter the category map to only include taxa that are in the taxa list - # included_category_map_with_taxa = [ - # category for category in category_map_with_taxa if category["taxon"] in taxa_in_list - # ] excluded_category_map_with_taxa = [ category for category in category_map_with_taxa if category["taxon"] not in taxa_in_list ] - # included_category_indices = [int(category["index"]) for category in category_map_with_taxa] excluded_category_indices = [ int(category["index"]) for category in excluded_category_map_with_taxa # type: ignore ] @@ -124,9 +118,6 @@ def make_classifications_filtered_by_taxa_list( timestamp = timezone.now() for classification in classifications: scores, logits = classification.scores, classification.logits - # Set scores and logits to zero if they are not in the filtered category indices - - import numpy as np # Assert that all scores & logits are lists of numbers if not isinstance(scores, list) or not all(isinstance(score, (int, float)) for score in scores): @@ -137,26 +128,17 @@ def make_classifications_filtered_by_taxa_list( logger.debug(f"Processing classification {classification.pk} with {len(scores)} scores") logger.info(f"Previous totals: {sum(scores)} scores, {sum(logits)} logits") - # scores_np_filtered = np.array(scores) logits_np = np.array(logits) - # scores_np_filtered[excluded_category_indices] = 0.0 - - # @TODO can we use np.NAN instead of 0.0? zero will NOT calculate correctly in softmax. - # @TODO delete the excluded categories from the scores and logits instead of setting to 0.0 - # logits_np[excluded_category_indices] = 0.0 - # logits_np[excluded_category_indices] = np.nan + # Mask excluded logits with -100 (effectively zero probability after softmax) + # @TODO consider using -np.inf for mathematically exact masking logits_np[excluded_category_indices] = -100 logits: list[float] = logits_np.tolist() - from numpy import exp - from numpy import sum as np_sum - - # @TODO add test to see if this is correct, or needed! # Recalculate the softmax scores based on the filtered logits - scores_np: np.ndarray = exp(logits_np - np.max(logits_np)) # Subtract max for numerical stability - scores_np /= np_sum(scores_np) # Normalize to get probabilities + scores_np: np.ndarray = np.exp(logits_np - np.max(logits_np)) # Subtract max for numerical stability + scores_np /= np.sum(scores_np) # Normalize to get probabilities scores: list = scores_np.tolist() # Convert back to list @@ -164,11 +146,8 @@ def make_classifications_filtered_by_taxa_list( # Get the taxon with the highest score using the index of the max score top_index = scores.index(max(scores)) - top_taxon = category_map_with_taxa[top_index][ - "taxon" - ] # @TODO: This doesn't work if the taxon has never been classified - print("Top taxon: ", category_map_with_taxa[top_index]) # @TODO: REMOVE - print("Top index: ", top_index) # @TODO: REMOVE + top_taxon = category_map_with_taxa[top_index]["taxon"] + logger.debug(f"Top taxon: {category_map_with_taxa[top_index]}, index: {top_index}") # check if needs updating if classification.scores == scores and classification.logits == logits: @@ -189,12 +168,15 @@ def make_classifications_filtered_by_taxa_list( detection=classification.detection, timestamp=classification.timestamp, terminal=True, - category_map=None, # @TODO need a new category map with the filtered taxa + category_map=new_algorithm.category_map, created_at=timestamp, updated_at=timestamp, ) if new_classification.taxon is None: - raise (ValueError("Classification isn't registered yet. Aborting")) # @TODO remove or fail gracefully + raise ValueError( + f"Unable to determine top taxon after class masking for classification {classification.pk}. " + "No allowed classes found in taxa list." + ) classifications_to_update.append(classification) classifications_to_add.append(new_classification) @@ -203,27 +185,27 @@ def make_classifications_filtered_by_taxa_list( assert new_classification.detection.occurrence is not None occurrences_to_update.add(new_classification.detection.occurrence) - logging.info( + logger.info( f"Adding new classification for Taxon {top_taxon} to occurrence {new_classification.detection.occurrence}" ) - # Bulk update the existing classifications - if classifications_to_update: - logger.info(f"Bulk updating {len(classifications_to_update)} existing classifications") - Classification.objects.bulk_update(classifications_to_update, ["terminal", "updated_at"]) - logger.info(f"Updated {len(classifications_to_update)} existing classifications") - - if classifications_to_add: - # Bulk create the new classifications - logger.info(f"Bulk creating {len(classifications_to_add)} new classifications") - Classification.objects.bulk_create(classifications_to_add) - logger.info(f"Added {len(classifications_to_add)} new classifications") - - # Update the occurrence determinations - logger.info(f"Updating the determinations for {len(occurrences_to_update)} occurrences") - for occurrence in occurrences_to_update: - occurrence.save(update_determination=True) - logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") + # Bulk update/create in a single transaction for atomicity + with transaction.atomic(): + if classifications_to_update: + logger.info(f"Bulk updating {len(classifications_to_update)} existing classifications") + Classification.objects.bulk_update(classifications_to_update, ["terminal", "updated_at"]) + logger.info(f"Updated {len(classifications_to_update)} existing classifications") + + if classifications_to_add: + logger.info(f"Bulk creating {len(classifications_to_add)} new classifications") + Classification.objects.bulk_create(classifications_to_add) + logger.info(f"Added {len(classifications_to_add)} new classifications") + + # Update the occurrence determinations + logger.info(f"Updating the determinations for {len(occurrences_to_update)} occurrences") + for occurrence in occurrences_to_update: + occurrence.save(update_determination=True) + logger.info(f"Updated determinations for {len(occurrences_to_update)} occurrences") class ClassMaskingTask(BasePostProcessingTask): diff --git a/ami/ml/post_processing/rank_rollup.py b/ami/ml/post_processing/rank_rollup.py index 9708b1c63..677c5ae1f 100644 --- a/ami/ml/post_processing/rank_rollup.py +++ b/ami/ml/post_processing/rank_rollup.py @@ -44,7 +44,8 @@ def run(self) -> None: # ---- Read config parameters ---- config = self.config or {} collection_id = config.get("source_image_collection_id") - thresholds = config.get("thresholds", self.DEFAULT_THRESHOLDS) + raw_thresholds = config.get("thresholds", self.DEFAULT_THRESHOLDS) + thresholds = {k.upper(): v for k, v in raw_thresholds.items()} rollup_order = config.get("rollup_order", self.ROLLUP_ORDER) if not collection_id: @@ -68,7 +69,8 @@ def run(self) -> None: with transaction.atomic(): for i, clf in enumerate(qs.iterator(), start=1): - self.logger.info(f"Processing classification #{clf.pk} (taxon={clf.taxon}, score={clf.score:.3f})") + score_str = f"{clf.score:.3f}" if clf.score is not None else "N/A" + self.logger.info(f"Processing classification #{clf.pk} (taxon={clf.taxon}, score={score_str})") if not clf.scores: self.logger.info(f"Skipping classification #{clf.pk}: no scores available") @@ -101,9 +103,6 @@ def run(self) -> None: self.logger.info(f"Aggregated taxon scores: {scores_str}") for rank in rollup_order: threshold = thresholds.get(rank, 1.0) - # import pdb - - # pdb.set_trace() candidates = {t: s for t, s in taxon_scores.items() if t.rank == rank} if not candidates: @@ -136,11 +135,14 @@ def run(self) -> None: ) occurrence = clf.detection.occurrence - updated_occurrences.append(occurrence) - self.logger.info( - f"Rolled up occurrence {occurrence.pk}: {clf.taxon} => {new_taxon} " - f"({new_taxon.rank}) with rolled-up score={new_score:.3f}" - ) + if occurrence: + updated_occurrences.append(occurrence) + self.logger.info( + f"Rolled up occurrence {occurrence.pk}: {clf.taxon} => {new_taxon} " + f"({new_taxon.rank}) with rolled-up score={new_score:.3f}" + ) + else: + self.logger.warning(f"Detection #{clf.detection.pk} has no occurrence; skipping.") else: self.logger.info(f"No rollup applied for classification #{clf.pk} (taxon={clf.taxon})") diff --git a/ami/ml/tests.py b/ami/ml/tests.py index aecf8859f..5563452f7 100644 --- a/ami/ml/tests.py +++ b/ami/ml/tests.py @@ -931,7 +931,7 @@ def _create_occurrences_with_classifications(self, num=3): detection=det, taxon=self.species_taxon, score=0.5, - scores=[0.5, 0.3, 0.2], + scores=[0.5, 0.2, 0.1], terminal=True, timestamp=now, algorithm=self.algorithm, @@ -975,7 +975,7 @@ def test_rank_rollup_creates_new_terminal_classifications(self): task = RankRollupTask( source_image_collection_id=self.collection.pk, - thresholds={"species": 0.8, "genus": 0.6, "family": 0.4}, + thresholds={"SPECIES": 0.8, "GENUS": 0.6, "FAMILY": 0.4}, ) task.run() From da9b0816f6ff3aa964e49f36be9dde48ecde0a95 Mon Sep 17 00:00:00 2001 From: Michael Bunsen Date: Wed, 18 Feb 2026 04:23:47 -0800 Subject: [PATCH 39/44] feat: replace hardcoded admin action with dynamic class masking form Replace the hardcoded `update_with_newfoundland_species` admin action with a generic `run_class_masking` action that shows an intermediate confirmation page. Users can now select any TaxaList and Algorithm from dropdowns before running class masking on selected occurrences. Co-Authored-By: Claude --- ami/main/admin.py | 107 +++++++++++------- .../main/class_masking_confirmation.html | 60 ++++++++++ 2 files changed, 126 insertions(+), 41 deletions(-) create mode 100644 ami/templates/admin/main/class_masking_confirmation.html diff --git a/ami/main/admin.py b/ami/main/admin.py index 4d7d76c09..e6ddce103 100644 --- a/ami/main/admin.py +++ b/ami/main/admin.py @@ -2,9 +2,11 @@ from django.contrib import admin from django.db import models +from django.db.models import Count from django.db.models.query import QuerySet from django.http.request import HttpRequest from django.template.defaultfilters import filesizeformat +from django.template.response import TemplateResponse from django.urls import reverse from django.utils.formats import number_format from django.utils.html import format_html @@ -426,59 +428,82 @@ def get_queryset(self, request: HttpRequest) -> QuerySet[Any]: def detections_count(self, obj) -> int: return obj.detections_count - @admin.action(description="Update occurrence with Newfoundland species taxa list") - def update_with_newfoundland_species(self, request: HttpRequest, queryset: QuerySet[Occurrence]) -> None: + @admin.action(description="Run class masking (select taxa list & algorithm)") + def run_class_masking(self, request: HttpRequest, queryset: QuerySet[Occurrence]) -> TemplateResponse | None: """ - Update selected occurrences using the 'Newfoundland species' taxa list - and 'Quebec & Vermont Species Classifier - Apr 2024' algorithm. + Run class masking on selected occurrences. + Shows an intermediate page to select a TaxaList and Algorithm. """ - try: - # Get the taxa list by name - taxa_list = TaxaList.objects.get(name="Newfoundland Species") - except TaxaList.DoesNotExist: - self.message_user( - request, - "Error: TaxaList 'Newfoundland species' not found.", - level="error", - ) - return - - try: - # Get the algorithm by name - algorithm = Algorithm.objects.get(name="Quebec & Vermont Species Classifier - Apr 2024") - except Algorithm.DoesNotExist: - self.message_user( - request, - "Error: Algorithm 'Quebec & Vermont Species Classifier - Apr 2024' not found.", - level="error", - ) - return + if request.POST.get("confirm"): + taxa_list_id = request.POST.get("taxa_list") + algorithm_id = request.POST.get("algorithm") + if not taxa_list_id or not algorithm_id: + self.message_user(request, "Please select both a taxa list and an algorithm.", level="error") + return None - # Process each occurrence - count = 0 - for occurrence in queryset: try: - update_single_occurrence( - occurrence=occurrence, - algorithm=algorithm, - taxa_list=taxa_list, - ) - count += 1 - except Exception as e: + taxa_list = TaxaList.objects.get(pk=taxa_list_id) + algorithm = Algorithm.objects.get(pk=algorithm_id) + except (TaxaList.DoesNotExist, Algorithm.DoesNotExist) as e: + self.message_user(request, f"Error: {e}", level="error") + return None + + if not algorithm.category_map: self.message_user( - request, - f"Error processing occurrence {occurrence.pk}: {str(e)}", - level="error", + request, f"Algorithm '{algorithm.name}' does not have a category map.", level="error" ) - - self.message_user(request, f"Successfully updated {count} occurrence(s).") + return None + + count = 0 + for occurrence in queryset: + try: + update_single_occurrence( + occurrence=occurrence, + algorithm=algorithm, + taxa_list=taxa_list, + ) + count += 1 + except Exception as e: + self.message_user( + request, + f"Error processing occurrence {occurrence.pk}: {e}", + level="error", + ) + + self.message_user(request, f"Successfully ran class masking on {count} occurrence(s).") + return None + + # Show intermediate confirmation page + taxa_lists = TaxaList.objects.annotate(taxa_count=Count("taxa")).filter(taxa_count__gt=0).order_by("name") + algorithms = Algorithm.objects.filter(category_map__isnull=False).order_by("name") + + # Annotate algorithms with label count + alg_list = [] + for alg in algorithms: + alg.labels_count = len(alg.category_map.labels) if alg.category_map else 0 + alg_list.append(alg) + + return TemplateResponse( + request, + "admin/main/class_masking_confirmation.html", + { + **self.admin_site.each_context(request), + "title": "Run class masking", + "queryset": queryset, + "occurrence_count": queryset.count(), + "taxa_lists": taxa_lists, + "algorithms": alg_list, + "opts": self.model._meta, + "action_checkbox_name": admin.helpers.ACTION_CHECKBOX_NAME, + }, + ) ordering = ("-created_at",) # Add classifications as inline inlines = [DetectionInline] - actions = [update_with_newfoundland_species] + actions = [run_class_masking] @admin.register(Classification) diff --git a/ami/templates/admin/main/class_masking_confirmation.html b/ami/templates/admin/main/class_masking_confirmation.html new file mode 100644 index 000000000..8c37ec361 --- /dev/null +++ b/ami/templates/admin/main/class_masking_confirmation.html @@ -0,0 +1,60 @@ +{% extends "admin/base_site.html" %} + +{% load i18n admin_urls %} + +{% block title %} + {% translate "Run class masking" %} | {{ site_title|default:_("Django site admin") }} +{% endblock title %} +{% block breadcrumbs %} +
+ {% translate 'Home' %} + › Occurrences + › {% translate 'Run class masking' %} +
+{% endblock breadcrumbs %} +{% block content %} +
+ {% csrf_token %} +

+ You are about to run class masking on {{ occurrence_count }} selected occurrence{{ occurrence_count|pluralize }}. +

+

+ This will: +

    +
  • Mark existing terminal classifications as non-terminal
    • +
  • + Create new classifications with logits masked for species not in the selected taxa list +
    • +
  • Update occurrence determinations based on the new top predictions
    • +
+

+
+ Select parameters +
+ + +

Species checklist to use as a mask. Only species in this list will remain active.

+
+
+ + +

The classifier algorithm whose predictions to mask. Must have a category map with logits.

+
+
+ {% for obj in queryset %}{% endfor %} + + +
+ + {% translate 'Cancel' %} +
+
+{% endblock content %} From fc3f9e183cbd8445793fb63abfa02b03d92fa518 Mon Sep 17 00:00:00 2001 From: Michael Bunsen Date: Wed, 18 Feb 2026 04:29:04 -0800 Subject: [PATCH 40/44] docs: add class masking screenshots for PR review Co-Authored-By: Claude --- docs/screenshots/admin-class-masking-form.png | Bin 0 -> 60949 bytes .../admin-classifications-after-masking.png | Bin 0 -> 557779 bytes .../ui-occurrence-43498-identification.png | Bin 0 -> 67423 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/screenshots/admin-class-masking-form.png create mode 100644 docs/screenshots/admin-classifications-after-masking.png create mode 100644 docs/screenshots/ui-occurrence-43498-identification.png diff --git a/docs/screenshots/admin-class-masking-form.png b/docs/screenshots/admin-class-masking-form.png new file mode 100644 index 0000000000000000000000000000000000000000..ac67a5949d320572315c46273dddf824f99eeb61 GIT binary patch literal 60949 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=?UTF-8?q?=E2=80=94=20N+1=20query,=20distinct,=20HTML,=20test=20ordering?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Pre-load label→taxon mapping in RankRollupTask to eliminate N×M queries - Add .distinct() to rank_rollup queryset (M2M join can produce dupes) - Replace len(classifications) with pre-computed classification_count - Fix invalid HTML: move