Display embeddings (#459)

cli/cli/handlers/finder.py

@@ -1,7 +1,6 @@
 from typing import Optional
 
 from cli.handlers.errors import handle_errors
-from winnow.utils.logging import configure_logging_cli
 
 
 class FinderCli:
@@ -13,30 +12,22 @@ def __init__(self, pipeline):
     @handle_errors
     def local_matches(self):
         """Find matches between local videos."""
-        from winnow.pipeline.generate_local_matches import generate_local_matches
-        from winnow.utils.files import scan_videos
+        import luigi
 
-        config = self._pipeline.config
-        configure_logging_cli(config.logging)
+        from winnow.pipeline.luigi.matches import MatchesReportTask
 
-        videos = scan_videos(config.sources.root, "**", extensions=config.sources.extensions)
-        generate_local_matches(files=videos, pipeline=self._pipeline)
+        luigi.build([MatchesReportTask(config=self._pipeline.config)], local_scheduler=True, workers=1)
 
-    def remote_matches(self, repo: Optional[str] = None, contributor: Optional[str] = None):
+    def remote_matches(self, repo: Optional[str] = None):
         """Find matches between local files and remote fingerprints."""
-        from winnow.pipeline.generate_remote_matches import generate_remote_matches
+        import logging.config
+        import luigi
 
-        config = self._pipeline.config
-        configure_logging_cli(config.logging)
+        from winnow.pipeline.luigi.matches import RemoteMatchesTask
 
-        if repo is not None:
-            repo = str(repo)
-
-        if contributor is not None:
-            contributor = str(contributor)
-
-        generate_remote_matches(
-            pipeline=self._pipeline,
-            repository_name=repo,
-            contributor_name=contributor,
+        logging.config.fileConfig("./logging.conf")
+        luigi.build(
+            [RemoteMatchesTask(config=self._pipeline.config, repository_name=repo)],
+            local_scheduler=True,
+            workers=1,
         )

cli/cli/handlers/pipeline.py

@@ -1,6 +1,3 @@
-import os
-
-
 class PipelineCli:
     """Process video files."""
 
@@ -9,20 +6,24 @@ def __init__(self, config):
 
     def all(self):
         """Process all video files."""
-        from winnow.utils.logging import configure_logging_cli
-        from winnow.pipeline.detect_scenes import detect_scenes
-        from winnow.pipeline.generate_local_matches import generate_local_matches
-        from winnow.utils.files import scan_videos
-        from winnow.pipeline.extract_exif import extract_exif
-        from winnow.pipeline.pipeline_context import PipelineContext
 
-        configure_logging_cli(self._config.logging)
+        import luigi
 
-        # Resolve list of video files from the directory
-        absolute_root = os.path.abspath(self._config.sources.root)
-        videos = scan_videos(absolute_root, "**", extensions=self._config.sources.extensions)
+        from winnow.pipeline.luigi.exif import ExifTask
+        from winnow.pipeline.luigi.signatures import (
+            SignaturesTask,
+            DBSignaturesTask,
+        )
+        from winnow.pipeline.luigi.matches import MatchesReportTask, DBMatchesTask
 
-        pipeline_context = PipelineContext(self._config)
-        generate_local_matches(files=videos, pipeline=pipeline_context)
-        detect_scenes(files=videos, pipeline=pipeline_context)
-        extract_exif(videos, pipeline=pipeline_context)
+        luigi.build(
+            [
+                ExifTask(config=self._config),
+                SignaturesTask(config=self._config),
+                DBSignaturesTask(config=self._config),
+                MatchesReportTask(config=self._config),
+                DBMatchesTask(config=self._config),
+            ],
+            local_scheduler=True,
+            workers=1,
+        )

docker-compose.yml

@@ -153,6 +153,7 @@ services:
       SECURITY_STORAGE_PATH: "/project/data/representations"
       RPC_SERVER_HOST: "rpc"
       RPC_SERVER_PORT: 50051
+      EMBEDDINGS_FOLDER: "/project/data/representations/embeddings"
     volumes:
       # Set the BENETECH_DATA_LOCATION environment variable to the path
       # on your host machine where you placed your video files

environment-gpu.yaml

@@ -51,6 +51,7 @@ dependencies:
       - trimap
       - pacmap
       - torch
+      - typing_extensions==4.1.1
       - grpcio==1.43.0
       - grpcio-tools==1.43.0
 

environment.yaml

@@ -52,6 +52,7 @@ dependencies:
       - trimap
       - pacmap
       - torch
+      - typing_extensions==4.1.1
       - grpcio==1.43.0
       - grpcio-tools==1.43.0
 

process_video_url.py

@@ -5,10 +5,7 @@
 import luigi
 
 from winnow.pipeline.luigi.download import DownloadFilesTask
-from winnow.pipeline.pipeline_context import PipelineContext
-from winnow.pipeline.process_urls import process_urls
 from winnow.utils.config import resolve_config
-from winnow.utils.logging import configure_logging_cli
 
 
 @click.command()

rpc/embeddings.py

@@ -0,0 +1,110 @@
+from threading import Lock
+from typing import List, Dict, Optional, Tuple
+
+import numpy as np
+from annoy import AnnoyIndex
+
+import rpc.rpc_pb2 as proto
+from winnow.pipeline.luigi.condense import CondensedFingerprints
+from winnow.pipeline.luigi.embeddings import (
+    EmbeddingsTask,
+    UmapEmbeddingsTask,
+    TSNEEmbeddingsTask,
+    TriMapEmbeddingsTask,
+    PaCMAPEmbeddingsTask,
+)
+from winnow.pipeline.luigi.embeddings_annoy_index import (
+    EmbeddingsAnnoyIndexTask,
+    PaCMAPAnnoyIndexTask,
+    TriMAPAnnoyIndexTask,
+    UMAPAnnoyIndexTask,
+    TSNEAnnoyIndexTask,
+)
+from winnow.pipeline.luigi.utils import FileKeyDF
+from winnow.pipeline.pipeline_context import PipelineContext
+from winnow.storage.file_key import FileKey
+
+
+class EmbeddingsIndex:
+    def __init__(self, annoy_index: AnnoyIndex, files: List[FileKey], positions: Dict[FileKey, np.ndarray]):
+        self._annoy_index: AnnoyIndex = annoy_index
+        self._files: List[FileKey] = files
+        self._positions: Dict[FileKey, np.ndarray] = positions
+
+    def query(
+        self,
+        x: float,
+        y: float,
+        max_count: int = 10,
+        max_distance: Optional[float] = None,
+    ) -> List[proto.FoundNeighbor]:
+        if max_distance <= 0:
+            max_distance = None
+        indices, distances = self._annoy_index.get_nns_by_vector([x, y], max_count, include_distances=True)
+        files = [self._files[i] for i in indices]
+        results: List[proto.FoundNeighbor] = []
+        for file, distance in zip(files, distances):
+            if max_distance is not None and distance > max_distance:
+                break
+            x, y = self._positions[file]
+            results.append(
+                proto.FoundNeighbor(
+                    file_path=file.path,
+                    file_hash=file.hash,
+                    distance=distance,
+                    x=x,
+                    y=y,
+                )
+            )
+        return results
+
+
+class EmbeddingLoader:
+    def __init__(self, pipeline: PipelineContext):
+        self._pipeline: PipelineContext = pipeline
+        self._cache: Dict[str, EmbeddingsIndex] = {}
+        self._lock = Lock()
+
+    def load(self, algorithm: str) -> Optional[EmbeddingsIndex]:
+        with self._lock:
+            if algorithm not in self._cache:
+                index = self._do_load(algorithm)
+                if index is not None:
+                    self._cache[algorithm] = index
+            return self._cache.get(algorithm)
+
+    def _do_load(self, algorithm: str) -> Optional[EmbeddingsIndex]:
+        """Do load embeddings index."""
+        embeddings_task, annoy_task = self._task(algorithm)
+        if embeddings_task is None or annoy_task is None:
+            return None
+        embeddings: CondensedFingerprints = embeddings_task.output().read()
+        if embeddings is None:
+            return None
+
+        annoy_output = annoy_task.output()
+        annoy_paths, _ = annoy_output.latest_result
+        if annoy_paths is None:
+            return None
+
+        annoy_index_path, annoy_files_path = annoy_paths
+        annoy_index = AnnoyIndex(2, "euclidean")
+        annoy_index.load(annoy_index_path)
+        annoy_files_df = FileKeyDF.read_csv(annoy_files_path)
+        positions: Dict[FileKey, np.ndarray] = {}
+        for i, file_key in enumerate(embeddings.to_file_keys()):
+            positions[file_key] = embeddings.fingerprints[i]
+        return EmbeddingsIndex(annoy_index, FileKeyDF.to_file_keys(annoy_files_df), positions)
+
+    def _task(self, algorithm: str) -> Tuple[Optional[EmbeddingsTask], Optional[EmbeddingsAnnoyIndexTask]]:
+        config = self._pipeline.config
+        if algorithm == "pacmap":
+            return PaCMAPEmbeddingsTask(config=config), PaCMAPAnnoyIndexTask(config=config)
+        elif algorithm == "trimap":
+            return TriMapEmbeddingsTask(config=config), TriMAPAnnoyIndexTask(config=config)
+        elif algorithm == "umap":
+            return UmapEmbeddingsTask(config=config), UMAPAnnoyIndexTask(config=config)
+        elif algorithm == "t-sne":
+            return TSNEEmbeddingsTask(config=config), TSNEAnnoyIndexTask(config=config)
+        else:
+            return None, None

rpc/rpc.proto

@@ -40,4 +40,35 @@ message StatusRequest {
 // Service status response
 message StatusResponse {
   bool status = 1;
+}
+
+// Embeddings service
+service Embeddings {
+  // Get nearest neighbors
+  rpc query_nearest_neighbors (NearestNeighborsRequest) returns (NearestNeighborsResults) {}
+  rpc get_status (EmbeddingsStatusRequest) returns (StatusResponse) {}
+}
+
+message NearestNeighborsRequest {
+  string algorithm = 1;
+  float x = 2;
+  float y = 3;
+  float max_distance = 4;
+  int32 max_count = 5;
+}
+
+message NearestNeighborsResults {
+  repeated FoundNeighbor neighbors = 1;
+}
+
+message FoundNeighbor {
+  string file_path = 1;
+  string file_hash = 2;
+  float x = 3;
+  float y = 4;
+  float distance = 5;
+}
+
+message EmbeddingsStatusRequest {
+  string algorithm = 1;
 }