diff --git a/.github/workflows/ci-winnow-uts.yml b/.github/workflows/ci-winnow-uts.yml index 232a0433..7afab71d 100644 --- a/.github/workflows/ci-winnow-uts.yml +++ b/.github/workflows/ci-winnow-uts.yml @@ -28,14 +28,14 @@ jobs: - name: Check formatting with black run: | # Check formatting of the dedup-app python files - black --line-length 120 --check *.py db winnow tests task_queue remote security template_support thumbnail cli rpc --exclude='rpc_pb2*' + black --line-length 120 --check *.py db winnow tests task_queue remote security template_support thumbnail cli - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - flake8 winnow db tests task_queue remote security template_support thumbnail cli rpc --exclude 'rpc_pb2*' --count --select=E9,F63,F7,F82 --show-source --statistics + flake8 winnow db tests task_queue remote security template_support thumbnail cli --count --select=E9,F63,F7,F82 --show-source --statistics # --ignore=E203 as it is not PEP8 and black style compliant. See: https://github.com/psf/black/issues/315 # Also --ignore=W503 as it is not compatible with black style. - flake8 winnow db tests task_queue remote security template_support thumbnail cli rpc --exclude 'rpc_pb2*' --count --max-complexity=10 --ignore=E203,W503 --max-line-length=120 --statistics + flake8 winnow db tests task_queue remote security template_support thumbnail cli --count --max-complexity=10 --ignore=E203,W503 --max-line-length=120 --statistics - name: Test with pytest run: | export PYTHONPATH="$(pwd)" diff --git a/.gitignore b/.gitignore index 50c58ef0..c8a0590d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,4 +9,3 @@ winnow/feature_extraction/pretrained_models/** notebooks/.ipynb_checkpoints/ tests/test_data/test_output/ .env -winnow/text_search/models/** \ No newline at end of file diff --git a/cli/cli/handlers/db_getter.py b/cli/cli/handlers/db_getter.py index 10bc94e9..459810f4 100644 --- a/cli/cli/handlers/db_getter.py +++ b/cli/cli/handlers/db_getter.py @@ -53,7 +53,7 @@ def files( req.audio = boolean("audio", audio) req.min_length = valid_duration_millis("min_length", min_length) req.max_length = valid_duration_millis("max_length", max_length) - req.include = [FileInclude.EXIF, FileInclude.SCENES, FileInclude.META] + req.include = [FileInclude.exif, FileInclude.scenes, FileInclude.meta] req.extensions = extensions req.date_from = valid_date("date_from", date_from) req.date_to = valid_date("date_to", date_to) @@ -69,7 +69,7 @@ def files( database = Database.from_uri(self._config.database.uri) with database.session_scope(expunge=True) as session: results = FilesDAO.list_files(req, session) - files = [Transform.file(item.file) for item in results.items] + files = [Transform.file(file) for file in results.items] formatter = resolve_formatter(format=output) formatter.format( files, fields, file=sys.stdout, highlights={"path": path, "hash": hash, "hash_short": hash} diff --git a/cli/cli/handlers/pipeline.py b/cli/cli/handlers/pipeline.py index 1d6937ea..a8978317 100644 --- a/cli/cli/handlers/pipeline.py +++ b/cli/cli/handlers/pipeline.py @@ -1,5 +1,8 @@ import os +from winnow.pipeline.extract_exif import extract_exif +from winnow.pipeline.pipeline_context import PipelineContext + class PipelineCli: """Process video files.""" @@ -13,8 +16,6 @@ def all(self): 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() diff --git a/cli/security b/cli/security deleted file mode 120000 index 872fbdf6..00000000 --- a/cli/security +++ /dev/null @@ -1 +0,0 @@ -../security \ No newline at end of file diff --git a/db/access/files.py b/db/access/files.py index 37ebe5c3..e49c4049 100644 --- a/db/access/files.py +++ b/db/access/files.py @@ -2,7 +2,7 @@ import enum import itertools from datetime import datetime -from typing import List, Optional, Iterator, Set, Iterable, Collection +from typing import List, Optional, Iterator, Set from dataclasses import dataclass from sqlalchemy import or_, and_, func, literal_column, tuple_ @@ -51,7 +51,7 @@ class FileInclude(enum.Enum): TEMPLATES = "templates" -# Some included data are mapped to the File fields. +# Some of the included data are mapped to the File fields. _FILE_FIELDS = { FileInclude.EXIF: Files.exif, FileInclude.META: Files.meta, @@ -68,8 +68,8 @@ def get_file_fields(included_fields: Set[FileInclude]): class ListFilesRequest: """Parameters for list-files query.""" - limit: Optional[int] = 20 - offset: Optional[int] = 0 + limit: int = 20 + offset: int = 0 path_query: str = None extensions: List[str] = () exif: bool = None @@ -78,7 +78,7 @@ class ListFilesRequest: max_length: int = None date_from: datetime = None date_to: datetime = None - include: Collection[FileInclude] = () + include: List[FileInclude] = () sort: Optional[FileSort] = None match_filter: FileMatchFilter = FileMatchFilter.ALL related_distance: float = 0.4 @@ -242,27 +242,23 @@ class FilesDAO: _countable_match = aliased(Matches) @staticmethod - def list_files( - req: ListFilesRequest, session, entity=Files, selected_ids: Iterable[int] = None - ) -> ListFilesResults: + def list_files(req: ListFilesRequest, session) -> ListFilesResults: """Query multiple files.""" # Count files - query = session.query(Files.id) - query = FilesDAO._filter_ids(query, selected_ids) + query = session.query(Files) query = FilesDAO._filter_by_file_attributes(req, query) counts = FilesDAO.counts(query, req.related_distance, req.duplicate_distance) # Select files included_values = FilesDAO._make_loaders(req) - query = session.query(entity, *(included.value for included in included_values)) - query = FilesDAO._filter_ids(query, selected_ids) + query = session.query(Files, *(included.value for included in included_values)) query = FilesDAO._filter_by_file_attributes(req, query) query = FilesDAO._filter_by_matches(req, query) query = FilesDAO._advice_query(query, req, included_values) query = FilesDAO._sort_items(req, query) - query = FilesDAO._apply_limit_offset(req, query) # Retrieve slice + query = query.offset(req.offset).limit(req.limit) items = FilesDAO._collect_items(query.all(), included_values) return ListFilesResults(items=items, counts=counts) @@ -299,15 +295,6 @@ def file_matches(file_id, session: Session, *, false_positive=False) -> Query: query = query.filter(Matches.false_positive == false_positive) return query - @staticmethod - def _apply_limit_offset(req: ListFilesRequest, query: Query) -> Query: - """Apply limit and offset params.""" - if req.limit is not None: - query = query.limit(req.limit) - if req.offset is not None: - query = query.offset(req.offset) - return query - @staticmethod def _make_loaders(req: ListFilesRequest, loader_types=_VALUE_LOADERS) -> List[QueryValueLoader]: """Get loaders for required values.""" @@ -332,7 +319,7 @@ def _collect_items(items: List, included_values: List[QueryValueLoader]) -> List # Each item is a File entity return list(map(FileData, items)) - # Otherwise, each item is a tuple of values + # Otherwise each item is a tuple of values result = [] for item in items: file_data = FileData(file=item[0]) @@ -368,13 +355,6 @@ def _sort_items(req: ListFilesRequest, query: Query) -> Query: ) return query - @staticmethod - def _filter_ids(query: Query, selected_ids: Iterable[int] = None) -> Query: - """Ensure ids are from the provided collection.""" - if selected_ids is not None: - query = query.filter(Files.id.in_(tuple(selected_ids))) - return query - @staticmethod def _filter_path(req: ListFilesRequest, query: Query) -> Query: """Filter by file name.""" @@ -516,13 +496,6 @@ def query_local_files(session: Session, path_hash_pairs) -> Query: query = query.filter(tuple_(Files.file_path, Files.sha256).in_(tuple(path_hash_pairs))) return query - @staticmethod - def query_local_file_ids(session: Session, path_hash_pairs) -> Query: - """Query local files by (path, hash) pairs.""" - query = session.query(Files.id).filter(Files.contributor == None) # noqa: E711 - query = query.filter(tuple_(Files.file_path, Files.sha256).in_(tuple(path_hash_pairs))) - return query - @staticmethod def query_remote_files(session: Session, repository_name: str = None, contributor_name: str = None) -> Query: """Query remote signatures from database.""" diff --git a/docker-compose.yml b/docker-compose.yml index 842e29bc..abc5c1c2 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -69,7 +69,7 @@ services: redis: image: "redis:alpine" ports: - - "6379:6379" + - 6379:6379 networks: - postgres-compose-network @@ -108,40 +108,6 @@ services: networks: - postgres-compose-network - rpc: - image: "johnhbenetech/videodeduplication:${BENETECH_RUNTIME:-gpu}${BENETECH_MODE}" - build: - context: . - dockerfile: "docker/Dockerfile.dedup-${BENETECH_RUNTIME:-gpu}" - target: prod - args: - GIT_HASH: "${GIT_HASH:-UNKNOWN}" - runtime: "${BENETECH_DOCKER_RUNTIME:-nvidia}" - command: bash -ic "python -m rpc.server" - environment: - WINNOW_CONFIG: "/project/config/config.yaml" - RPC_SERVER_PORT: 50051 - RPC_SERVER_HOST: rpc - ports: - - "50051:50051" - volumes: - # Set the BENETECH_DATA_LOCATION environment variable to the path - # on your host machine where you placed the source data - - "${BENETECH_DATA_LOCATION:?\n\nPlease set \"BENETECH_DATA_LOCATION\" environment variable to the root folder of your video files.}:/project/data" - # You may want to set BENETECH_TASK_LOGS environment variable to - # keep pipeline logs in a specific directory in your host fs. - - "${BENETECH_TASK_LOGS:-pipeline-logs}:/project/pipeline-logs" - # You can specify BENETECH_FILE_STORAGE_DIRECTORY environment variable to - # keep template examples in a specific directory in your host fs. - - "${BENETECH_FILE_STORAGE_DIRECTORY:-file-storage}:/project/file-storage" - # You can specify BENETECH_CONFIG_DIRECTORY environment variable to - # keep application configs in a specific directory in your host fs. - - "${BENETECH_CONFIG_DIRECTORY:-config}:/project/config" - depends_on: - - postgres - networks: - - postgres-compose-network - server: image: "johnhbenetech/videodeduplication:server${BENETECH_MODE}" build: diff --git a/environment-gpu.yaml b/environment-gpu.yaml index 971a0a5d..5998bc04 100644 --- a/environment-gpu.yaml +++ b/environment-gpu.yaml @@ -31,7 +31,7 @@ dependencies: - pyyaml - cached-property - dataclasses - - celery==5.0.5 + - celery - redis - billiard - librosa @@ -39,11 +39,8 @@ dependencies: - inquirer - pytimeparse - annoy - - yt-dlp + - youtube-dl - dacite - deprecation - - torch - - grpcio==1.43.0 - - grpcio-tools==1.43.0 prefix: C:\ProgramData\Anaconda3\envs\winnow diff --git a/environment.yaml b/environment.yaml index 3edd960e..f16fbcca 100644 --- a/environment.yaml +++ b/environment.yaml @@ -32,7 +32,7 @@ dependencies: - pyyaml - cached-property - dataclasses - - celery==5.0.5 + - celery - redis - billiard - librosa @@ -40,11 +40,8 @@ dependencies: - inquirer - pytimeparse - annoy - - yt-dlp + - youtube-dl - dacite - deprecation - - torch - - grpcio==1.43.0 - - grpcio-tools==1.43.0 prefix: C:\ProgramData\Anaconda3\envs\winnow diff --git a/notebooks/Winnow Text Search Demonstration.ipynb b/notebooks/Winnow Text Search Demonstration.ipynb deleted file mode 100644 index b51d44c5..00000000 --- a/notebooks/Winnow Text Search Demonstration.ipynb +++ /dev/null @@ -1,678 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "27d30153-785f-47e3-8443-e1cb3aa27491", - "metadata": {}, - "source": [ - "## Text Search Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f504adb7-215a-409e-85bd-d0122a295e69", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7027d38a-5359-49d8-9f75-1dad49dc10cf", - "metadata": {}, - "outputs": [], - "source": [ - "from winnow.text_search.main_utils import get_search_engine,query_signatures,load_search_space_from_signatures,VideoSearch" - ] - }, - { - "cell_type": "markdown", - "id": "46f89664-8b59-434a-bae8-cf4a6320fbbd", - "metadata": {}, - "source": [ - "The simplest form to text the text search module is to have it running with a set of extracted frames and signatures." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "17cb5c33-171c-4279-bb21-d2992d3538c6", - "metadata": {}, - "outputs": [], - "source": [ - "# As generated by the project's feature extraction pipeline\n", - "SIGNATURES_FOLDER = \"../data/syria/video_signatures\"\n", - "FRAMES_FOLDER = \"../data/syria/frames\"" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4c9bec78-fc8b-47ed-9251-78551ecc4762", - "metadata": {}, - "outputs": [], - "source": [ - "vs = VideoSearch(SIGNATURES_FOLDER)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2a55480a-1186-433d-8428-8395481d9205", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.032 seconds\n", - "\n" - ] - } - ], - "source": [ - "files, distances,query_data = vs.query('people in the streets')" - ] - }, - { - "cell_type": "markdown", - "id": "95888a3d-01d1-4022-96e3-79615886ff2b", - "metadata": {}, - "source": [ - "### List of files and distances aligned and sorted" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3611f90a-e169-42e2-a576-7ce8a26a7abc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('45687b6c401743acbd1af67ce720f689.ogv', 0.02912245),\n", - " ('f64024c0d2a94efeb8fbfe2e860e3e9e.mp4', 0.02550951),\n", - " ('b1f6d81fa22449289720015281b05bc8.webm', 0.024763409),\n", - " ('c9d8c700aa8d40b692882986f9ebd146.ogv', 0.024474367)]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(zip(files,distances))[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "217ed208-c035-41a6-924f-e3a1c7a5463a", - "metadata": {}, - "source": [ - "### Context information about how the query was processed and fed into our model" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a3818201-1735-49c0-81a8-217655af828a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'original_query': 'people in the streets',\n", - " 'tokens': ['people', 'streets'],\n", - " 'clean_tokens': ['people', 'streets'],\n", - " 'human_readable': 'people streets',\n", - " 'score': 1.0}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_data" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "b7b9b69b-ebc3-47bd-917e-4a78a62340d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.02912245, 0.02550951, 0.024763409, 0.024474367, 0.02307131)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distances[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "6736f1d7-bc70-48e7-8058-340e0cec6b7a", - "metadata": {}, - "source": [ - "Clean tokens shows what actually gets passed into the text encoder. " - ] - }, - { - "cell_type": "markdown", - "id": "0493bbc1-9d03-4b38-829b-c091bea752d4", - "metadata": {}, - "source": [ - "## What happens if a query contains out of vocab words / typos?" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "387d47f0-9883-4d5f-8938-ef9393455e32", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.018 seconds\n", - "\n" - ] - } - ], - "source": [ - "files, distances,query_data = vs.query('street people')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f9ce9994-2b37-43d7-ad6a-d7418ea2e086", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'original_query': 'street people',\n", - " 'tokens': ['street', 'people'],\n", - " 'clean_tokens': ['street', 'people'],\n", - " 'human_readable': 'street people',\n", - " 'score': 1.0}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_data" - ] - }, - { - "cell_type": "markdown", - "id": "a8cd31a0-04c5-49c0-9cec-16295b13dac7", - "metadata": {}, - "source": [ - "Model ignores* the word Cairo, since it's not in the vocabulary.\n", - "\n", - "note: depending on the model configuration, out of vocab words might be mapped to multiple \"out_of_vocab\" entities (that would vary according to strategy used (word2Vec,bow,word hashing)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "39064e86-4132-481a-865c-c8ead70b7ce6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.011 seconds\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "({'original_query': 'Agfaqgfagagagagagag',\n", - " 'tokens': ['agfaqgfagagagagagag'],\n", - " 'clean_tokens': [],\n", - " 'human_readable': '',\n", - " 'score': 0.0},\n", - " (0.18101189, 0.15912527, 0.1558112, 0.14925787, 0.14747253))" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files, distances,query_data = vs.query('Agfaqgfagagagagagag')\n", - "query_data,distances[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "51082476-50d8-461c-9f49-ab39d5838152", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.011 seconds\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "({'original_query': 'Agfxqgfagagxgagagag',\n", - " 'tokens': ['agfxqgfagagxgagagag'],\n", - " 'clean_tokens': [],\n", - " 'human_readable': '',\n", - " 'score': 0.0},\n", - " (0.18101189, 0.15912527, 0.1558112, 0.14925787, 0.14747253))" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files, distances,query_data = vs.query('Agfxqgfagagxgagagag')\n", - "query_data,distances[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a0e2b583-11f3-4107-b9e6-8e41b955d78d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.022 seconds\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "({'original_query': 'Baafalfalfjlajfaljflafjl',\n", - " 'tokens': ['baafalfalfjlajfaljflafjl'],\n", - " 'clean_tokens': [],\n", - " 'human_readable': '',\n", - " 'score': 0.0},\n", - " (0.18101189, 0.15912527, 0.1558112, 0.14925787, 0.14747253))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files, distances,query_data = vs.query('Baafalfalfjlajfaljflafjl')\n", - "query_data,distances[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "9dcec629-cfdf-44c1-b5e8-5b28b5e0aac5", - "metadata": {}, - "source": [ - "\"Dirty\" queries should return the same results in general." - ] - }, - { - "cell_type": "markdown", - "id": "6a4d1a68-abaa-49c0-853a-271bc2d5ef72", - "metadata": {}, - "source": [ - "## Does word order matter?" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "1f87fad2-e064-4480-b364-369a4058ca4f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.017 seconds\n", - "\n" - ] - } - ], - "source": [ - "files, distances,query_data = vs.query('red flag')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "0cad6e2c-3667-417a-82ed-116ea9a1e319", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'original_query': 'red flag',\n", - " 'tokens': ['red', 'flag'],\n", - " 'clean_tokens': ['red', 'flag'],\n", - " 'human_readable': 'red flag',\n", - " 'score': 1.0}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_data" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "24ee3e8f-d4af-4eb3-9586-1979275a1150", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.12298576, 0.12203802, 0.121082336, 0.11644756, 0.11514723)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distances[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "8dd39dd9-496f-4b8a-af6c-a248bf73c812", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.014 seconds\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "{'original_query': 'flag red',\n", - " 'tokens': ['flag', 'red'],\n", - " 'clean_tokens': ['flag', 'red'],\n", - " 'human_readable': 'flag red',\n", - " 'score': 1.0}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files, distances,query_data = vs.query('flag red')\n", - "query_data" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "6b67ea45-7564-4fa2-a937-7e48b2feed5e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.113494426, 0.11275999, 0.111292556, 0.106624216, 0.105996415)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distances[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "5cda8149-2ed2-4e30-b2fe-3e51d0726344", - "metadata": {}, - "source": [ - "The model takes advantage of sequencial information, so queries with the same tokens in different order should get different results." - ] - }, - { - "cell_type": "markdown", - "id": "29947101-96d7-4d27-afef-4b5f023f1c8f", - "metadata": {}, - "source": [ - "## Low Level Api\n", - "\n", - "Example of how a \"probing\" function could be implemented." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "407d26f8-4e76-4e79-8d34-3881e8af71da", - "metadata": {}, - "outputs": [], - "source": [ - "from winnow.text_search.main_utils import get_signature_activations,get_query_context,load_model\n", - "from glob import glob\n", - "import os\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "f194eebc-da6e-47ba-a7a8-afe35e0e2ac6", - "metadata": {}, - "outputs": [], - "source": [ - "model = load_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "d72eae09-9d57-4fec-8bc1-81a7c6da56c8", - "metadata": {}, - "outputs": [], - "source": [ - "sample_signatures_file_paths = glob(os.path.join(SIGNATURES_FOLDER,\"**.npy\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "d28dbe18-4fe1-45e8-85b0-c94fcb6cb9bb", - "metadata": {}, - "outputs": [], - "source": [ - "# signature for a video known to have fire\n", - "sig = [x for x in sample_signatures_file_paths if \"b83cb57\" in x]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "e6eb784b-04ae-4247-aca4-feef161ac0ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 500)" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_signature = np.array([np.load(sig[0])])\n", - "sample_signature.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "c9d8e7f3-5c3f-415e-a30e-d883ff54eca0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cosine_sim execution time: 0.000 seconds\n", - "\n" - ] - } - ], - "source": [ - "query_for_fire = get_signature_activations(\"fire\",sample_signature,model)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "4eda0205-a7ac-4870-87f4-b642a4bf95a5", - "metadata": {}, - "outputs": [], - "source": [ - "concepts = \"fire hospital drugs street people desert plane tank weapons smoke sky bombs children blood\"" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "98bedca6-d6d2-4ee5-a3e2-dc35725a8f4f", - "metadata": {}, - "outputs": [], - "source": [ - "normalized_data = get_query_context(concepts)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "4df9b87a-4117-472a-83c6-885d58059c71", - "metadata": {}, - "outputs": [], - "source": [ - "meaningful_tokens = normalized_data['clean_tokens']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db348705-a81d-4f6f-b7aa-6dbf743eebd3", - "metadata": {}, - "outputs": [], - "source": [ - "sims = np.array([get_signature_activations(x,sample_signature,model) for x in meaningful_tokens])\n", - "normalized = (sims - (-1)) / (1 - (-1))" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "bffc85a7-84a8-44d4-bea0-254772a8f46c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'fire': 0.65911376,\n", - " 'hospital': 0.48177385,\n", - " 'drugs': 0.49717602,\n", - " 'street': 0.5205738,\n", - " 'people': 0.4893952,\n", - " 'desert': 0.5021971,\n", - " 'plane': 0.4601108,\n", - " 'tank': 0.5076987,\n", - " 'weapons': 0.5362744,\n", - " 'smoke': 0.54367775,\n", - " 'sky': 0.54045117,\n", - " 'bombs': 0.54835665,\n", - " 'children': 0.48668715,\n", - " 'blood': 0.49972978}" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(zip(meaningful_tokens,normalized[:,0]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/prepare_text_search.py b/prepare_text_search.py deleted file mode 100644 index 70c39f97..00000000 --- a/prepare_text_search.py +++ /dev/null @@ -1,22 +0,0 @@ -import click - -from winnow.pipeline.pipeline_context import PipelineContext -from winnow.pipeline.prepare_text_search import prepare_text_search -from winnow.utils.config import resolve_config -from winnow.utils.logging import configure_logging_cli - - -@click.command() -@click.option("--config", "-cp", help="path to the project config file", default=None) -@click.option("--force", "-f", help="Rebuild existing text search index", default=False, is_flag=True) -def main(config, force): - logger = configure_logging_cli() - logger.info("Loading config file") - config = resolve_config(config_path=config) - - pipeline = PipelineContext(config) - prepare_text_search(pipeline, force) - - -if __name__ == "__main__": - main() diff --git a/rpc/README.md b/rpc/README.md deleted file mode 100644 index a239d0fb..00000000 --- a/rpc/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# RPC Server - -Remote Procedure Call server is required to call machine-learning algorithms from -other services (e.g. from REST API server). - -## Running Server - -To run the rpc-server execute the following command: -```shell -python -m rpc.server -``` - -Recognized environment variables: - * `RPC_SERVER_HOST` - server host name - * `RPC_SERVER_PORT` - server port - * `RPC_SERVER_EAGER_INITIALIZE` - if defined the server will initialize video-index on startup (by default video-index is initialized on the first request) - -## Development - -To generate `rpc_pb2.py` (protocol implementation) and `rpc_pb2_grpc.py` (gRPC logic) -run the following command from the repository root: -```shell -python -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. rpc/rpc.proto -``` diff --git a/rpc/__init__.py b/rpc/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/rpc/errors.py b/rpc/errors.py deleted file mode 100644 index 570e9f45..00000000 --- a/rpc/errors.py +++ /dev/null @@ -1,15 +0,0 @@ -import grpc - - -class ServiceUnavailable(Exception): - """Service unavailable error.""" - - -def unavailable( - context: grpc.ServicerContext, - message: str = "Service is temporary unavailable", -) -> ServiceUnavailable: - """Construct exception and setup error details in gRPC context.""" - context.set_code(grpc.StatusCode.UNAVAILABLE) - context.set_details(message) - return ServiceUnavailable(message) diff --git a/rpc/logging.py b/rpc/logging.py deleted file mode 100644 index 7b2fd0fa..00000000 --- a/rpc/logging.py +++ /dev/null @@ -1,32 +0,0 @@ -import logging -import sys - - -def configure_logging( - level: int = logging.INFO, - error_log_file: str = "processing_error.log", - format: str = "[%(asctime)s: %(levelname)s] [%(name)s] %(message)s", -): - """Configure RPC server logging.""" - # configure winnow logger - - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setFormatter(logging.Formatter(format)) - console_handler.setLevel(level) - - winnow_logger = logging.getLogger("winnow") - winnow_logger.setLevel(level) - winnow_logger.addHandler(console_handler) - - rpc_logger = logging.getLogger("rpc") - rpc_logger.setLevel(level) - rpc_logger.addHandler(console_handler) - - main_logger = logging.getLogger("__main__") - main_logger.setLevel(level) - main_logger.addHandler(console_handler) - - # Write errors to the log file - error_log_handler = logging.FileHandler(error_log_file) - error_log_handler.setFormatter(logging.Formatter(format)) - error_log_handler.setLevel(logging.ERROR) diff --git a/rpc/rpc.proto b/rpc/rpc.proto deleted file mode 100644 index 22b0cacd..00000000 --- a/rpc/rpc.proto +++ /dev/null @@ -1,32 +0,0 @@ -syntax = "proto3"; - -package rpc.proto; - -// Semantic search service -service SemanticSearch { - // Perform semantic search by text description - rpc query_videos (TextSearchRequest) returns (TextSearchResults) {} -} - -// Semantic text search request -message TextSearchRequest { - string query = 1; - float min_similarity = 2; - int32 max_count = 3; -} - -// Search results entry -message FoundVideo { - uint64 id = 1; - float score = 2; -} - -// Result of the semantic text search -message TextSearchResults { - repeated FoundVideo videos = 1; - string original_query = 2; - repeated string tokens = 3; - repeated string clean_tokens = 4; - string human_readable = 5; - float score = 6; -} diff --git a/rpc/rpc_pb2.py b/rpc/rpc_pb2.py deleted file mode 100644 index 57051fe8..00000000 --- a/rpc/rpc_pb2.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# source: rpc/rpc.proto -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import message as _message -from google.protobuf import reflection as _reflection -from google.protobuf import symbol_database as _symbol_database -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - - - -DESCRIPTOR = _descriptor.FileDescriptor( - name='rpc/rpc.proto', - package='rpc.proto', - syntax='proto3', - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_pb=b'\n\rrpc/rpc.proto\x12\trpc.proto\"M\n\x11TextSearchRequest\x12\r\n\x05query\x18\x01 \x01(\t\x12\x16\n\x0emin_similarity\x18\x02 \x01(\x02\x12\x11\n\tmax_count\x18\x03 \x01(\x05\"\'\n\nFoundVideo\x12\n\n\x02id\x18\x01 \x01(\x04\x12\r\n\x05score\x18\x02 \x01(\x02\"\x9f\x01\n\x11TextSearchResults\x12%\n\x06videos\x18\x01 \x03(\x0b\x32\x15.rpc.proto.FoundVideo\x12\x16\n\x0eoriginal_query\x18\x02 \x01(\t\x12\x0e\n\x06tokens\x18\x03 \x03(\t\x12\x14\n\x0c\x63lean_tokens\x18\x04 \x03(\t\x12\x16\n\x0ehuman_readable\x18\x05 \x01(\t\x12\r\n\x05score\x18\x06 \x01(\x02\x32^\n\x0eSemanticSearch\x12L\n\x0cquery_videos\x12\x1c.rpc.proto.TextSearchRequest\x1a\x1c.rpc.proto.TextSearchResults\"\x00\x62\x06proto3' -) - - - - -_TEXTSEARCHREQUEST = _descriptor.Descriptor( - name='TextSearchRequest', - full_name='rpc.proto.TextSearchRequest', - filename=None, - file=DESCRIPTOR, - containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[ - _descriptor.FieldDescriptor( - name='query', full_name='rpc.proto.TextSearchRequest.query', index=0, - number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='min_similarity', full_name='rpc.proto.TextSearchRequest.min_similarity', index=1, - number=2, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='max_count', full_name='rpc.proto.TextSearchRequest.max_count', index=2, - number=3, type=5, cpp_type=1, label=1, - has_default_value=False, default_value=0, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=28, - serialized_end=105, -) - - -_FOUNDVIDEO = _descriptor.Descriptor( - name='FoundVideo', - full_name='rpc.proto.FoundVideo', - filename=None, - file=DESCRIPTOR, - containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[ - _descriptor.FieldDescriptor( - name='id', full_name='rpc.proto.FoundVideo.id', index=0, - number=1, type=4, cpp_type=4, label=1, - has_default_value=False, default_value=0, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='score', full_name='rpc.proto.FoundVideo.score', index=1, - number=2, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=107, - serialized_end=146, -) - - -_TEXTSEARCHRESULTS = _descriptor.Descriptor( - name='TextSearchResults', - full_name='rpc.proto.TextSearchResults', - filename=None, - file=DESCRIPTOR, - containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[ - _descriptor.FieldDescriptor( - name='videos', full_name='rpc.proto.TextSearchResults.videos', index=0, - number=1, type=11, cpp_type=10, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='original_query', full_name='rpc.proto.TextSearchResults.original_query', index=1, - number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='tokens', full_name='rpc.proto.TextSearchResults.tokens', index=2, - number=3, type=9, cpp_type=9, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='clean_tokens', full_name='rpc.proto.TextSearchResults.clean_tokens', index=3, - number=4, type=9, cpp_type=9, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='human_readable', full_name='rpc.proto.TextSearchResults.human_readable', index=4, - number=5, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - _descriptor.FieldDescriptor( - name='score', full_name='rpc.proto.TextSearchResults.score', index=5, - number=6, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=149, - serialized_end=308, -) - -_TEXTSEARCHRESULTS.fields_by_name['videos'].message_type = _FOUNDVIDEO -DESCRIPTOR.message_types_by_name['TextSearchRequest'] = _TEXTSEARCHREQUEST -DESCRIPTOR.message_types_by_name['FoundVideo'] = _FOUNDVIDEO -DESCRIPTOR.message_types_by_name['TextSearchResults'] = _TEXTSEARCHRESULTS -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -TextSearchRequest = _reflection.GeneratedProtocolMessageType('TextSearchRequest', (_message.Message,), { - 'DESCRIPTOR' : _TEXTSEARCHREQUEST, - '__module__' : 'rpc.rpc_pb2' - # @@protoc_insertion_point(class_scope:rpc.proto.TextSearchRequest) - }) -_sym_db.RegisterMessage(TextSearchRequest) - -FoundVideo = _reflection.GeneratedProtocolMessageType('FoundVideo', (_message.Message,), { - 'DESCRIPTOR' : _FOUNDVIDEO, - '__module__' : 'rpc.rpc_pb2' - # @@protoc_insertion_point(class_scope:rpc.proto.FoundVideo) - }) -_sym_db.RegisterMessage(FoundVideo) - -TextSearchResults = _reflection.GeneratedProtocolMessageType('TextSearchResults', (_message.Message,), { - 'DESCRIPTOR' : _TEXTSEARCHRESULTS, - '__module__' : 'rpc.rpc_pb2' - # @@protoc_insertion_point(class_scope:rpc.proto.TextSearchResults) - }) -_sym_db.RegisterMessage(TextSearchResults) - - - -_SEMANTICSEARCH = _descriptor.ServiceDescriptor( - name='SemanticSearch', - full_name='rpc.proto.SemanticSearch', - file=DESCRIPTOR, - index=0, - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_start=310, - serialized_end=404, - methods=[ - _descriptor.MethodDescriptor( - name='query_videos', - full_name='rpc.proto.SemanticSearch.query_videos', - index=0, - containing_service=None, - input_type=_TEXTSEARCHREQUEST, - output_type=_TEXTSEARCHRESULTS, - serialized_options=None, - create_key=_descriptor._internal_create_key, - ), -]) -_sym_db.RegisterServiceDescriptor(_SEMANTICSEARCH) - -DESCRIPTOR.services_by_name['SemanticSearch'] = _SEMANTICSEARCH - -# @@protoc_insertion_point(module_scope) diff --git a/rpc/rpc_pb2_grpc.py b/rpc/rpc_pb2_grpc.py deleted file mode 100644 index 58dfdeb3..00000000 --- a/rpc/rpc_pb2_grpc.py +++ /dev/null @@ -1,77 +0,0 @@ -# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! -"""Client and server classes corresponding to protobuf-defined services.""" -import grpc - -from rpc import rpc_pb2 as rpc_dot_rpc__pb2 - - -class SemanticSearchStub(object): - """Semantic search service""" - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.query_videos = channel.unary_unary( - "/rpc.proto.SemanticSearch/query_videos", - request_serializer=rpc_dot_rpc__pb2.TextSearchRequest.SerializeToString, - response_deserializer=rpc_dot_rpc__pb2.TextSearchResults.FromString, - ) - - -class SemanticSearchServicer(object): - """Semantic search service""" - - def query_videos(self, request, context): - """Perform semantic search by text description""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") - - -def add_SemanticSearchServicer_to_server(servicer, server): - rpc_method_handlers = { - "query_videos": grpc.unary_unary_rpc_method_handler( - servicer.query_videos, - request_deserializer=rpc_dot_rpc__pb2.TextSearchRequest.FromString, - response_serializer=rpc_dot_rpc__pb2.TextSearchResults.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler("rpc.proto.SemanticSearch", rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) - - -# This class is part of an EXPERIMENTAL API. -class SemanticSearch(object): - """Semantic search service""" - - @staticmethod - def query_videos( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, - target, - "/rpc.proto.SemanticSearch/query_videos", - rpc_dot_rpc__pb2.TextSearchRequest.SerializeToString, - rpc_dot_rpc__pb2.TextSearchResults.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) diff --git a/rpc/server.py b/rpc/server.py deleted file mode 100644 index eaed9eab..00000000 --- a/rpc/server.py +++ /dev/null @@ -1,102 +0,0 @@ -import logging -import os -from concurrent import futures - -import grpc -from google.protobuf.json_format import MessageToJson - -import rpc.rpc_pb2 as proto -import rpc.rpc_pb2_grpc as services -from rpc.errors import unavailable -from rpc.logging import configure_logging -from winnow.config.path import resolve_config_path -from winnow.pipeline.pipeline_context import PipelineContext, ComponentNotAvailable -from winnow.text_search.main_utils import VideoSearch -from winnow.utils.config import resolve_config - -logger = logging.getLogger("rpc.server") - - -class SemanticSearch(services.SemanticSearchServicer): - """Provides methods that implement functionality of the SemanticSearch""" - - def __init__(self, pipeline: PipelineContext): - self.pipeline: PipelineContext = pipeline - - def query_videos( - self, - request: proto.TextSearchRequest, - context: grpc.ServicerContext, - ) -> proto.TextSearchResults: - try: - search_engine = self._get_search_engine(context) - file_ids, scores, details = search_engine.query( - request.query, - min_similarity=request.min_similarity, - max_count=request.max_count, - ) - found = [] - for file_id, score in zip(file_ids, scores): - found.append(proto.FoundVideo(id=file_id, score=score)) - return proto.TextSearchResults( - videos=found, - original_query=details["original_query"], - tokens=details["tokens"], - clean_tokens=details["clean_tokens"], - human_readable=details["human_readable"], - score=details["score"], - ) - except Exception: - logger.exception("Exception while processing request: %s", MessageToJson(request)) - raise - - def _get_search_engine(self, context: grpc.ServicerContext) -> VideoSearch: - """Try to get search engine and gracefully handle exceptions.""" - try: - return self.pipeline.text_search_engine - except ComponentNotAvailable as error: - raise unavailable(context, str(error)) - - -def initialize_search_engine(pipeline: PipelineContext): - """Try to eagerly initialize semantic search engine.""" - logger.info("Trying to initialize semantic search engine.") - try: - engine = pipeline.text_search_engine - logger.info("Semantic search engine is initialized successfully.") - return engine - except ComponentNotAvailable: - logger.warning("Text search engine is not available. Did you forget to create index?") - - -def serve(host: str, port: int, pipeline: PipelineContext, eager: bool = False): - semantic_search = SemanticSearch(pipeline) - if eager: - initialize_search_engine(pipeline) - - listen_address = f"{host}:{port}" - server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) - services.add_SemanticSearchServicer_to_server(semantic_search, server) - server.add_insecure_port(listen_address) - logger.info("JusticeAI RPC server is initialized.") - - logger.info("Listening incoming connections on %s", listen_address) - server.start() - server.wait_for_termination() - - -def main(): - configure_logging() - config_path = resolve_config_path() - config = resolve_config(config_path) - pipeline = PipelineContext(config) - logger.info("Resolved config path: %s", config_path) - - host = os.environ.get("RPC_SERVER_HOST", "localhost") - port = os.environ.get("RPC_SERVER_PORT", "50051") - eager = "RPC_SERVER_EAGER_INITIALIZE" in os.environ - serve(host, port, pipeline, eager) - - -if __name__ == "__main__": - main() diff --git a/server/README.md b/server/README.md index 31ca8e1a..47209ccd 100644 --- a/server/README.md +++ b/server/README.md @@ -51,8 +51,6 @@ Server honors the following environment variables: * `ONLINE_POLICY` - Online detection policy. Possible values are `online`, `offline` and `detect` (default is `detect`) * `SECURITY_STORAGE_PATH` - Directory in which third-party credentials are stored (default is `./`). * `SECURITY_MASTER_KEY_PATH` - File path in which master key for third-party credentials is stored (unset by default). - * `RPC_SERVER_HOST` - Hostname of the RPC Server (RPC provides online data processing services, default is `localhost`) - * `RPC_SERVER_PORT` - RPC Server port Server accepts the following command-line arguments: @@ -72,8 +70,6 @@ Server accepts the following command-line arguments: * `--online_policy` - set server online detection policty (overrices `ONLINE_POLICY` variable) * `--security_storage_path` - set directory in which third-party credentials are stored (overrides `SECURITY_STORAGE_PATH` varialbe) * `--security_master_key_path` - set file path in which master key for third-party credentials is stored (overrides `SECURITY_MASTER_KEY_PATH` variable) - * `--rpc_server_host` - set the rpc-server host to connect to (overrides `RPC_SERVER_HOST` variable) - * `--rpc_server_port` - set the rpc-server port (overrides `RPC_SERVER_PORT` variable) ## Serving Frontend diff --git a/server/requirements.txt b/server/requirements.txt index a54fe8d8..c9a2b2c4 100644 --- a/server/requirements.txt +++ b/server/requirements.txt @@ -1,5 +1,4 @@ Flask==1.1.2 -itsdangerous==2.0.1 SQLAlchemy==1.3.21 Flask-SQLAlchemy==2.4.4 PyYAML==5.4 @@ -12,5 +11,3 @@ eventlet==0.31.0 Werkzeug==1.0.1 cached-property==1.5.2 cryptography==3.3.1 -grpcio==1.43.0 -grpcio-tools==1.43.0 diff --git a/server/rpc b/server/rpc deleted file mode 120000 index 64566490..00000000 --- a/server/rpc +++ /dev/null @@ -1 +0,0 @@ -../rpc \ No newline at end of file diff --git a/server/server/api/__init__.py b/server/server/api/__init__.py index e40018d5..7eda24f2 100644 --- a/server/server/api/__init__.py +++ b/server/server/api/__init__.py @@ -18,7 +18,6 @@ repositories, contributors, online, - processing, ) from .blueprint import api diff --git a/server/server/api/files.py b/server/server/api/files.py index 1b1164cb..3dcfaa41 100644 --- a/server/server/api/files.py +++ b/server/server/api/files.py @@ -2,21 +2,10 @@ from http import HTTPStatus from os.path import dirname, basename -import dataclasses -from dataclasses import dataclass from flask import jsonify, request, abort, send_from_directory -import rpc.rpc_pb2 as proto from db.access.fields import Fields -from db.access.files import ( - ListFilesRequest, - FileMatchFilter, - FileSort, - FilesDAO, - FileInclude, - get_file_fields, - ListFilesResults, -) +from db.access.files import ListFilesRequest, FileMatchFilter, FileSort, FilesDAO, FileInclude, get_file_fields from db.schema import Files from thumbnail.ffmpeg import extract_frame_tmp from .blueprint import api @@ -31,7 +20,6 @@ get_config, parse_int_list, parse_enum_seq, - semantic_search, ) from ..model import database, Transform @@ -39,19 +27,10 @@ FILE_FIELDS = Fields(Files.exif, Files.meta, Files.signature, Files.scenes) -@dataclass -class ListFilesApiRequest(ListFilesRequest): - """Augmented List-Files request.""" - - semantic_query: str = None - min_semantic_similarity: float = 0.05 - max_semantic_search_hits: int = 10000 - - -def parse_params() -> ListFilesApiRequest: +def parse_params() -> ListFilesRequest: """Parse and validate request arguments.""" config = get_config() - result = ListFilesApiRequest() + result = ListFilesRequest() result.limit = parse_positive_int(request.args, "limit", 20) result.offset = parse_positive_int(request.args, "offset", 0) result.path_query = request.args.get("path", "", type=str).strip() @@ -71,45 +50,14 @@ def parse_params() -> ListFilesApiRequest: result.repository = request.args.get("repository", None, type=str) result.templates = parse_int_list(request.args, "templates") result.contributors = parse_int_list(request.args, "contributors") - result.semantic_query = request.args.get("semantic_query", None, type=str) - result.min_semantic_similarity = request.args.get( - "min_semantic_similarity", result.min_semantic_similarity, type=float - ) - result.max_semantic_search_hits = parse_positive_int( - request.args, "max_semantic_search_hits", result.max_semantic_search_hits - ) return result -def handle_semantic_query(req: ListFilesApiRequest) -> ListFilesResults: - """Handle request with semantic video query.""" - with semantic_search() as service: - response = service.query_videos( - proto.TextSearchRequest( - query=req.semantic_query, - min_similarity=req.min_semantic_similarity, - max_count=req.max_semantic_search_hits, - ) - ) - selected_ids = [file.id for file in response.videos] - no_limits_req = dataclasses.replace(req, limit=None, offset=None, include=()) - result_ids = FilesDAO.list_files(no_limits_req, database.session, entity=Files.id, selected_ids=selected_ids) - limit, offset = req.limit, req.offset - page_ids = [item.file for item in result_ids.items][offset : offset + limit] - load_page_req = ListFilesRequest(include=req.include, sort=req.sort) - result_files = FilesDAO.list_files(load_page_req, database.session, selected_ids=page_ids) - result_files.counts = result_ids.counts - return result_files - - @api.route("/files/", methods=["GET"]) def list_files(): req = parse_params() - if req.semantic_query: - results = handle_semantic_query(req) - else: - results = FilesDAO.list_files(req, database.session) + results = FilesDAO.list_files(req, database.session) include_flags = {field.value: True for field in req.include} return jsonify( diff --git a/server/server/api/helpers.py b/server/server/api/helpers.py index de0eb70e..b4830c5c 100644 --- a/server/server/api/helpers.py +++ b/server/server/api/helpers.py @@ -1,16 +1,13 @@ import enum import os import re -from contextlib import contextmanager from datetime import datetime from functools import lru_cache from http import HTTPStatus -from typing import Type, Iterator +from typing import Type -import grpc from flask import current_app, abort -import rpc.rpc_pb2_grpc as services from db import Database from db.access.fields import Fields from remote.repository_dao_database import DBRemoteRepoDAO @@ -71,14 +68,6 @@ def get_repos_dao() -> DBRemoteRepoDAO: ) -@contextmanager -def semantic_search() -> Iterator[services.SemanticSearchStub]: - """Get semantic search service.""" - config = get_config() - with grpc.insecure_channel(config.rpc_server.address) as channel: - yield services.SemanticSearchStub(channel) - - def resolve_video_file_path(file_path): """Get path to the video file.""" config = get_config() diff --git a/server/server/api/processing.py b/server/server/api/processing.py deleted file mode 100644 index f3098bdc..00000000 --- a/server/server/api/processing.py +++ /dev/null @@ -1,19 +0,0 @@ -import grpc -from flask import jsonify, request -from google.protobuf.json_format import MessageToDict - -import rpc.rpc_pb2 as proto -import rpc.rpc_pb2_grpc as services -from .blueprint import api - - -@api.route("/processing/text", methods=["GET"]) -def processing_text(): - """List file extensions.""" - query: str = request.args.get("query") - - with grpc.insecure_channel("localhost:50051") as channel: - service = services.SemanticSearchStub(channel) - req = proto.TextSearchRequest(query=query, min_similarity=0.0, max_count=1000) - response = service.query_videos(req) - return jsonify(MessageToDict(response)) diff --git a/server/server/application.py b/server/server/application.py deleted file mode 100644 index e69de29b..00000000 diff --git a/server/server/config.py b/server/server/config.py index 35a1d92d..a1c6b4d1 100644 --- a/server/server/config.py +++ b/server/server/config.py @@ -22,18 +22,6 @@ def parse_enum_value(enum_cls: Type[enum.Enum], value, default): return enum_cls(value) -class RPCServerConfig: - """RPC-Server configuration.""" - - def __init__(self): - self.host = os.environ.get("RPC_SERVER_HOST", "localhost") - self.port = int(os.environ.get("RPC_SERVER_PORT", 50051)) - - @property - def address(self) -> str: - return f"{self.host}:{self.port}" - - class DatabaseConfig: """Database connection configuration.""" @@ -104,7 +92,6 @@ class Config: def __init__(self): self.database = DatabaseConfig() - self.rpc_server = RPCServerConfig() self.host = os.environ.get("SERVER_HOST", "0.0.0.0") self.port = int(os.environ.get("SERVER_PORT", 5000)) self.static_folder = os.environ.get("STATIC_FOLDER", "static") diff --git a/server/server/main.py b/server/server/main.py index d5ec059d..1df8a020 100644 --- a/server/server/main.py +++ b/server/server/main.py @@ -58,8 +58,6 @@ def serve( online_policy=None, security_storage_path=None, security_master_key_path=None, - rpc_server_host=None, - rpc_server_port=None, ): """Start Deduplication API Server.""" eventlet.monkey_patch() @@ -85,8 +83,6 @@ def serve( config.database.secret = db_secret or config.database.secret config.database.dialect = db_dialect or config.database.dialect config.database.override_uri = db_uri or config.database.override_uri - config.rpc_server.host = rpc_server_host or config.rpc_server.host - config.rpc_server.port = rpc_server_port or config.rpc_server.port # Create application application = create_application(config) diff --git a/web/.nvmrc b/web/.nvmrc index bf79505b..2ccd0b76 100644 --- a/web/.nvmrc +++ b/web/.nvmrc @@ -1 +1 @@ -v16.14.0 +v14.17.1 diff --git a/web/package-lock.json b/web/package-lock.json index d9fa54c3..580b0de6 100644 --- a/web/package-lock.json +++ b/web/package-lock.json @@ -10,7 +10,6 @@ "license": "MIT", "dependencies": { "@date-io/date-fns": "^1.3.13", - "@fontsource/roboto": "^4.5.3", "@material-ui/core": "^4.11.0", "@material-ui/icons": "^4.9.1", "@material-ui/lab": "^4.0.0-alpha.57", @@ -77,7 +76,7 @@ "@typescript-eslint/parser": "^4.32.0", "babel-eslint": "^10.1.0", "babel-loader": "^8.2.2", - "css-loader": "^6.6.0", + "css-loader": "^3.6.0", "cypress": "^8.6.0", "eslint": 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"sha512-KMvVuFzpKBuiIXW3E4u3mySRO2/mCHSyZDJQM5NQ9Q9KHWHWh0NHgfbRMLLrceUK5qAL4ytALJbpRMjixFZh8A==", - "dev": true, + "version": "7.4.6", + "resolved": "https://registry.npmjs.org/ws/-/ws-7.4.6.tgz", + "integrity": "sha512-YmhHDO4MzaDLB+M9ym/mDA5z0naX8j7SIlT8f8z+I0VtzsRbekxEutHSme7NPS2qE8StCYQNUnfWdXta/Yu85A==", "requires": {} }, "xml-name-validator": { @@ -29805,6 +32586,12 @@ "version": "0.1.2", "resolved": "https://registry.npmjs.org/yeast/-/yeast-0.1.2.tgz", "integrity": "sha1-AI4G2AlDIMNy28L47XagymyKxBk=" + }, + "yocto-queue": { + "version": "0.1.0", + "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", + "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==", + "dev": true } } } diff --git a/web/package.json b/web/package.json index e00fa7c8..36c1f647 100644 --- a/web/package.json +++ b/web/package.json @@ -48,7 +48,7 @@ "@typescript-eslint/parser": "^4.32.0", "babel-eslint": "^10.1.0", "babel-loader": "^8.2.2", - "css-loader": "^6.6.0", + "css-loader": "^3.6.0", "cypress": "^8.6.0", "eslint": "^7.5.0", "eslint-plugin-jest": "^24.1.3", @@ -69,7 +69,6 @@ }, "dependencies": { "@date-io/date-fns": "^1.3.13", - "@fontsource/roboto": "^4.5.3", "@material-ui/core": "^4.11.0", "@material-ui/icons": "^4.9.1", "@material-ui/lab": "^4.0.0-alpha.57", diff --git a/web/src/components/app/AppMenu/AppMenuHeader/AppMenuHeader.tsx b/web/src/components/app/AppMenu/AppMenuHeader/AppMenuHeader.tsx index dd791308..e183ebb7 100644 --- a/web/src/components/app/AppMenu/AppMenuHeader/AppMenuHeader.tsx +++ b/web/src/components/app/AppMenu/AppMenuHeader/AppMenuHeader.tsx @@ -102,7 +102,6 @@ type AppMenuHeaderProps = React.HTMLProps & { open: boolean; onToggle: () => void; className?: string; - "aria-controls"?: string; }; export default AppMenuHeader; diff --git a/web/src/i18n/locales/default.en-US.json b/web/src/i18n/locales/default.en-US.json index 88e9c02c..76725d35 100644 --- a/web/src/i18n/locales/default.en-US.json +++ b/web/src/i18n/locales/default.en-US.json @@ -226,11 +226,6 @@ "filter.template": "Matched templates", "filter.templates.help": "Select files matching specified templates", "filter.partners.help": "Display remote fingerprints pulled from selected partners.", - "filter.fileName": "File name", - "filter.semanticSearch": "Semantic search", - "filter.fileNameQuery": "File Name Query", - "filter.semanticQuery": "Semantic Query", - "filter.queryType": "Query Type", "preview.notAvailable": "Preview not available.", "match.load.error": "Error loading matches.", "match.notMatch": "Match not found.", diff --git a/web/src/index.tsx b/web/src/index.tsx index 098f959f..0f8d70ab 100644 --- a/web/src/index.tsx +++ b/web/src/index.tsx @@ -5,7 +5,6 @@ import defaultTheme from "./theme"; import { detectLocale, LocaleData } from "./i18n/locale"; import JusticeAI from "./JusticeAI/JusticeAI"; import Server from "./server-api/v1/Server"; -import "@fontsource/roboto"; const server = new Server(); const locale = detectLocale(); diff --git a/web/src/model/VideoFile.ts b/web/src/model/VideoFile.ts index e9f21c26..40fccabd 100644 --- a/web/src/model/VideoFile.ts +++ b/web/src/model/VideoFile.ts @@ -32,15 +32,6 @@ export enum MatchCategory { unique = "unique", } -/** - * Semantic search filters. - */ -export type SemanticFilters = { - query: string; - minSimilarity?: number; - maxHits?: number; -}; - /** * File query filters. */ @@ -55,7 +46,6 @@ export type FileFilters = { remote: boolean | null; templates: Template["id"][]; contributors: Contributor["id"][]; - semantic: SemanticFilters; }; /** @@ -72,9 +62,6 @@ export const DefaultFilters: FileFilters = { remote: false, templates: [], contributors: [], - semantic: { - query: "", - }, }; /** diff --git a/web/src/pages/FileBrowserPage/FileBrowserPage.tsx b/web/src/pages/FileBrowserPage/FileBrowserPage.tsx index 3cc803af..d06d2cef 100644 --- a/web/src/pages/FileBrowserPage/FileBrowserPage.tsx +++ b/web/src/pages/FileBrowserPage/FileBrowserPage.tsx @@ -77,17 +77,13 @@ function FileBrowserPage(props: FileBrowserPageProps): JSX.Element { }, [showFilters, showFiltersRef]); // Handle query params - const handleNameQuery = useCallback( + const handleQuery = useCallback( (query) => coll.updateParams({ query, }), [] ); - const handleSemanticQuery = useCallback( - (query: string) => coll.updateParams({ semantic: { query } }), - [] - ); const updateCategory = useCallback( (matches) => coll.updateParams({ @@ -110,10 +106,8 @@ function FileBrowserPage(props: FileBrowserPageProps): JSX.Element {
& { * Handle close button. */ onClose: () => void; - "aria-controls"?: string; - className?: string; }; export default FilterPaneHeader; diff --git a/web/src/pages/FileBrowserPage/SearchTextInput.tsx b/web/src/pages/FileBrowserPage/SearchTextInput.tsx index f823f687..fd84d1fc 100644 --- a/web/src/pages/FileBrowserPage/SearchTextInput.tsx +++ b/web/src/pages/FileBrowserPage/SearchTextInput.tsx @@ -1,13 +1,7 @@ -import React, { - Dispatch, - SetStateAction, - useCallback, - useEffect, - useState, -} from "react"; +import React, { useCallback, useEffect, useState } from "react"; import clsx from "clsx"; import { makeStyles } from "@material-ui/styles"; -import { MenuItem, Select, Theme } from "@material-ui/core"; +import { Theme } from "@material-ui/core"; import FormControl from "@material-ui/core/FormControl"; import InputLabel from "@material-ui/core/InputLabel"; import OutlinedInput from "@material-ui/core/OutlinedInput"; @@ -15,193 +9,108 @@ import useUniqueId from "../../lib/hooks/useUniqueId"; import { useIntl } from "react-intl"; import InputAdornment from "@material-ui/core/InputAdornment"; import IconButton from "@material-ui/core/IconButton"; +import SearchOutlinedIcon from "@material-ui/icons/SearchOutlined"; import ClearOutlinedIcon from "@material-ui/icons/ClearOutlined"; const useStyles = makeStyles(() => ({ - container: { + input: { backgroundColor: "#EBEBEB", - display: "flex", - alignItems: "center", - }, - textControl: { - flexGrow: 2, - }, - textInput: { - borderRadius: "4px 0px 0px 4px", - borderRight: "none", - }, - selectInput: { - borderLeftWidth: 0, - borderRadius: "0px 4px 4px 0px", - minWidth: 200, }, })); function useMessages() { const intl = useIntl(); return { - search: intl.formatMessage({ id: "actions.searchFingerprints" }), - fileName: intl.formatMessage({ id: "filter.fileName" }), - semantic: intl.formatMessage({ id: "filter.semanticSearch" }), - nameQuery: intl.formatMessage({ id: "filter.fileNameQuery" }), - semanticQuery: intl.formatMessage({ id: "filter.semanticQuery" }), - queryType: intl.formatMessage({ id: "filter.queryType" }), + search: intl.formatMessage({ + id: "actions.searchFingerprints", + }), }; } -type UseQuery = { - staging: string; - setStaging: Dispatch>; - onChange: (query: string) => void; - handleChange: (event: any) => void; - forceSearch: () => void; - clear: () => void; -}; - -function useQuery(value: string, onSearch: (query: string) => void): UseQuery { - const [staging, setStaging] = useState(value); +function SearchTextInput(props: SearchTextInputProps): JSX.Element { + const { query: queryAttr = "", onSearch, className } = props; + const classes = useStyles(); + const inputId = useUniqueId("search-input"); + const messages = useMessages(); + const [query, setQuery] = useState(queryAttr); const [timeoutHandle, setTimeoutHandle] = useState( null ); - useEffect(() => { - setStaging(value); + const handleChange = useCallback((event) => setQuery(event.target.value), []); + const handleClear = useCallback(() => setQuery(""), []); + const handleSearch = useCallback(() => { if (timeoutHandle != null) { clearTimeout(timeoutHandle); - setTimeoutHandle(null); } - }, [value]); - - const onChange = useCallback( - (newStaging) => { - setStaging(newStaging); - if (timeoutHandle != null) { - clearTimeout(timeoutHandle); - } - const newTimeoutHandle = setTimeout(() => onSearch(newStaging), 1000); - setTimeoutHandle(newTimeoutHandle); - }, - [timeoutHandle, onSearch] - ); - - const handleChange = useCallback( - (event) => onChange(event.target.value), - [onChange] - ); - - const forceSearch = useCallback(() => { - if (timeoutHandle != null) { - clearTimeout(timeoutHandle); - setTimeoutHandle(null); + if (query !== queryAttr && onSearch != null) { + onSearch(query); } - onSearch(staging); - }, [staging, onSearch, timeoutHandle]); - - const clear = useCallback(() => onChange(""), [onChange]); - - return { staging, setStaging, handleChange, onChange, forceSearch, clear }; -} - -enum QueryType { - Semantic, - FileName, -} - -type SearchTextInputProps = { - nameQuery: string; - onNameSearch: (query: string) => void; - semanticQuery: string; - onSemanticSearch: (query: string) => void; - className?: string; -}; - -export default function SearchTextInput( - props: SearchTextInputProps -): JSX.Element { - const { - nameQuery, - onNameSearch, - semanticQuery, - onSemanticSearch, - className, - } = props; - const classes = useStyles(); - const inputId = useUniqueId("search-input"); - const selectId = useUniqueId("type-select"); - const messages = useMessages(); - const [queryType, setQueryType] = useState(QueryType.FileName); - - const nameSearch = useQuery(nameQuery, onNameSearch); - const semanticSearch = useQuery(semanticQuery, onSemanticSearch); - const search = queryType == QueryType.FileName ? nameSearch : semanticSearch; - - const handleQueryType = useCallback((event) => { - setQueryType(event.target.value); - }, []); + }, [query, onSearch, timeoutHandle]); const handleControlKeys = useCallback( (event) => { if (event.key === "Enter") { - search.forceSearch(); + handleSearch(); } else if (event.key === "Escape") { - search.clear(); + handleClear(); } }, - [search] + [handleSearch] ); - const label = - queryType == QueryType.FileName - ? messages.nameQuery - : messages.semanticQuery; + useEffect(() => { + if (timeoutHandle != null) { + clearTimeout(timeoutHandle); + } + const newHandle = setTimeout(handleSearch, 1000); + setTimeoutHandle(newHandle); + return () => clearTimeout(newHandle); + }, [query, onSearch]); - const labelWidth = queryType == QueryType.FileName ? 119 : 115; + useEffect(() => { + if (query !== queryAttr) { + setQuery(queryAttr); + } + }, [queryAttr]); return ( -
- - {label} - - {search.staging && ( - - - - )} - - } - labelWidth={labelWidth} - onKeyDown={handleControlKeys} - className={classes.textInput} - /> - - - {messages.queryType} - - -
+ + {messages.search} + + {!query && } + {query && ( + + + + )} + + } + labelWidth={145} + onKeyDown={handleControlKeys} + /> + ); } + +type SearchTextInputProps = { + query?: string; + onSearch?: (query: string) => void; + className?: string; +}; +export default SearchTextInput; diff --git a/web/src/pages/VideoDetailsPage/FileInfoPanel.tsx b/web/src/pages/VideoDetailsPage/FileInfoPanel.tsx index 69bd1fe6..dcf06aaf 100644 --- a/web/src/pages/VideoDetailsPage/FileInfoPanel.tsx +++ b/web/src/pages/VideoDetailsPage/FileInfoPanel.tsx @@ -28,6 +28,5 @@ type FileInfoPanelProps = React.HTMLProps & { * Video file */ file: VideoFile; - className?: string; }; export default FileInfoPanel; diff --git a/web/src/pages/VideoDetailsPage/TimeCaption.tsx b/web/src/pages/VideoDetailsPage/TimeCaption.tsx index ef53e131..e0784dc0 100644 --- a/web/src/pages/VideoDetailsPage/TimeCaption.tsx +++ b/web/src/pages/VideoDetailsPage/TimeCaption.tsx @@ -21,7 +21,6 @@ type TimeCaptionProps = React.HTMLProps & { * Time in milliseconds. */ time: number; - className?: string; }; export default TimeCaption; diff --git a/web/src/server-api/v1/transform/FilesTransformer.ts b/web/src/server-api/v1/transform/FilesTransformer.ts index baa01a2f..6c4f3be2 100644 --- a/web/src/server-api/v1/transform/FilesTransformer.ts +++ b/web/src/server-api/v1/transform/FilesTransformer.ts @@ -105,18 +105,6 @@ export default class FilesTransformer { if (filters?.contributors != null && filters.contributors.length > 0) { params.contributors = filters.contributors.join(","); } - if ( - filters?.semantic.query != null && - filters?.semantic.query.trim().length > 0 - ) { - params.semantic_query = filters.semantic.query.trim(); - } - if (filters?.semantic.maxHits != null) { - params.max_semantic_search_hits = filters.semantic.maxHits; - } - if (filters?.semantic.minSimilarity != null) { - params.min_semantic_similarity = filters.semantic.minSimilarity; - } return params; } diff --git a/web/src/theme/theme.ts b/web/src/theme/theme.ts index 9385e6da..db6af6eb 100644 --- a/web/src/theme/theme.ts +++ b/web/src/theme/theme.ts @@ -1,4 +1,4 @@ -import { createTheme, Theme } from "@material-ui/core"; +import { createMuiTheme, Theme } from "@material-ui/core"; import palette from "./palette"; import mixins from "./mixins"; import dimensions, { DimensionOptions, Dimensions } from "./dimensions"; @@ -18,7 +18,7 @@ declare module "@material-ui/core" { * * @see https://material-ui.com/customization/theming */ -const theme: Theme = createTheme({ +const theme: Theme = createMuiTheme({ palette, mixins, dimensions, diff --git a/web/webpack.config.js b/web/webpack.config.js index 4832c987..d62b0e6a 100644 --- a/web/webpack.config.js +++ b/web/webpack.config.js @@ -33,13 +33,18 @@ module.exports = { }, { test: /\.(jpe?g|gif|png|svg|woff(2)?|ttf)$/, - type: "asset/resource", + use: [ + { + loader: "file-loader", + options: {}, + }, + ], }, { test: /i18n\/locales\/[^/]*\.json$/, loader: "file-loader", type: "javascript/auto", - exclude: /default\.[^/]*\.json$/, + exclude: [/default\.[^/]*\.json$/], }, { test: /\.css$/i, diff --git a/winnow/duplicate_detection/annoy_neighbors.py b/winnow/duplicate_detection/annoy_neighbors.py index 8a5f19d6..43086245 100644 --- a/winnow/duplicate_detection/annoy_neighbors.py +++ b/winnow/duplicate_detection/annoy_neighbors.py @@ -1,5 +1,5 @@ -import numpy as np from annoy import AnnoyIndex +import numpy as np class AnnoyNNeighbors: @@ -55,7 +55,7 @@ def fit(self, data): annoy_index.add_item(i, list(v)) if self.optimize_trees: - self.n_trees = self._optimize(data) + self.n_trees = self.__optimize__(data, self.optimize_trees) self.search_k = self.search_k_intensity * (self.n_trees * self.n_neighbors) annoy_index.build(self.n_trees) @@ -72,7 +72,7 @@ def kneighbors(self, data, optimize=False, return_distance=True, search_k_intens if optimize: - self.n_neighbors = self._optimize(data) + self.n_neighbors = self.__optimize__(data) results = [ self.annoy_index.get_nns_by_vector( @@ -98,6 +98,8 @@ def kneighbors(self, data, optimize=False, return_distance=True, search_k_intens return distances, indices - @staticmethod - def _optimize(data): - return int(np.sqrt(len(data)) / 2) + def __optimize__(self, data): + + trees = int(np.sqrt(len(data)) / 2) + + return trees diff --git a/winnow/pipeline/extract_exif.py b/winnow/pipeline/extract_exif.py index 29745f45..b0dd04bd 100644 --- a/winnow/pipeline/extract_exif.py +++ b/winnow/pipeline/extract_exif.py @@ -1,6 +1,6 @@ import logging from os.path import join -from typing import Iterable, Optional +from typing import Iterable from db.schema import Files from winnow.pipeline.pipeline_context import PipelineContext @@ -8,18 +8,10 @@ from winnow.storage.db_result_storage import DBResultStorage from winnow.storage.repr_utils import path_resolver from winnow.utils.files import scan_videos, get_hash -from winnow.utils.metadata_extraction import ( - extract_from_list_of_videos, - convert_to_df, - parse_and_filter_metadata_df, -) - - -def extract_exif( - videos: Optional[Iterable[str]], - pipeline: PipelineContext, - progress_monitor=ProgressMonitor.NULL, -): +from winnow.utils.metadata_extraction import extract_from_list_of_videos, convert_to_df, parse_and_filter_metadata_df + + +def extract_exif(videos: Iterable[str], pipeline: PipelineContext, progress_monitor=ProgressMonitor.NULL): """Extract EXIF metadata from video files.""" logger = logging.getLogger(__name__) diff --git a/winnow/pipeline/pipeline_context.py b/winnow/pipeline/pipeline_context.py index 5b24c3c3..2ac8e64f 100644 --- a/winnow/pipeline/pipeline_context.py +++ b/winnow/pipeline/pipeline_context.py @@ -24,25 +24,14 @@ ) from winnow.storage.repr_storage import ReprStorage from winnow.storage.repr_utils import path_resolver -from winnow.text_search.main_utils import load_model as load_text_search_model, VideoSearch -from winnow.text_search.model import CrossModalNetwork -from winnow.text_search.similarity_index import AnnoySimilarityIndex, SimilarityIndex from winnow.utils.repr import repr_storage_factory, filekey_resolver logger = logging.getLogger(__name__) -class ComponentNotAvailable(Exception): - """Error indicating component is not available.""" - - class PipelineContext: """Pipeline components created and wired consistently according to the pipeline Config.""" - TEXT_SEARCH_INDEX_NAME = "text_search_annoy_index" - TEXT_SEARCH_DATABASE_IDS_NAME = "text_search_database_ids" - TEXT_SEARCH_N_FEATURES = 2048 - def __init__(self, config: Config): """Create pipeline context.""" self._config = config @@ -148,37 +137,3 @@ def make_client(self, repo: RemoteRepository) -> RepositoryClient: def file_storage(self) -> FileStorage: """Create file storage for template examples.""" return LocalFileStorage(directory=self.config.file_storage.directory) - - def text_search_id_index_exists(self) -> bool: - """Check if text index exists.""" - return AnnoySimilarityIndex.exists( - directory=self.config.repr.directory, - index_name=self.TEXT_SEARCH_INDEX_NAME, - ids_name=self.TEXT_SEARCH_DATABASE_IDS_NAME, - ) - - @cached_property - def text_search_model(self) -> CrossModalNetwork: - """Get prepared semantic text search model.""" - return load_text_search_model() - - @cached_property - def text_search_id_index(self) -> SimilarityIndex: - """Get semantic search index.""" - index = AnnoySimilarityIndex() - try: - index.load( - directory=self.config.repr.directory, - index_name=self.TEXT_SEARCH_INDEX_NAME, - ids_name=self.TEXT_SEARCH_DATABASE_IDS_NAME, - n_features=self.TEXT_SEARCH_N_FEATURES, - allow_pickle=True, - ) - except FileNotFoundError: - raise ComponentNotAvailable("Semantic text search index not found. Did you forget to create one?") - return index - - @cached_property - def text_search_engine(self) -> VideoSearch: - """Get semantic text search engine.""" - return VideoSearch(self.text_search_id_index, self.text_search_model) diff --git a/winnow/pipeline/prepare_text_search.py b/winnow/pipeline/prepare_text_search.py deleted file mode 100644 index 42d5f55f..00000000 --- a/winnow/pipeline/prepare_text_search.py +++ /dev/null @@ -1,63 +0,0 @@ -import logging - -import numpy as np -from dataclasses import astuple - -from db.access.files import FilesDAO -from winnow.pipeline.pipeline_context import PipelineContext -from winnow.pipeline.progress_monitor import ProgressMonitor -from winnow.text_search.similarity_index import AnnoySimilarityIndex -from winnow.utils.iterators import chunks - - -def prepare_text_search( - pipeline: PipelineContext, - force: bool = True, - progress: ProgressMonitor = ProgressMonitor.NULL, -): - """Build text similarity index for existing fingerprints.""" - logger = logging.getLogger(__name__) - progress.scale(1.0) - - # Skip index building if not required - if not force and pipeline.text_search_id_index_exists(): - logger.info("Text search index already exists.") - progress.complete() - return - elif force and pipeline.text_search_id_index_exists(): - logger.info("Rebuilding text search index because force=True") - elif not pipeline.text_search_id_index_exists(): - logger.info("Text-search is missing and will be created.") - - logger.info("Loading fingerprints.") - fingerprint_storage = pipeline.repr_storage.signature - file_keys, fingerprints = [], [] - for file_key in fingerprint_storage.list(): - fingerprint = fingerprint_storage.read(file_key) - file_keys.append(file_key) - fingerprints.append(fingerprint) - progress.increase(0.3) - logger.info("Loaded %s fingerprints", len(fingerprints)) - - logger.info("Getting database ids") - file_ids = [] - for chunk in chunks(file_keys, size=10000): - with pipeline.database.session_scope() as session: - ids_chunk = FilesDAO.query_local_file_ids(session, map(astuple, chunk)).all() - file_ids.extend(ids_chunk) - logger.info("Loaded %s database ids", len(file_ids)) - progress.increase(0.1) - - logger.info("Loading the text search model") - model = pipeline.text_search_model - - logger.info("Converting fingerprints using the text-search model") - vectors = np.array([model.embed_vis(fingerprint)[0].numpy() for fingerprint in fingerprints]) - - logger.info("Building annoy text search index.") - index = AnnoySimilarityIndex() - index.fit(file_ids, np.array(vectors)) - - logger.info("Saving text-search index.") - index.save(pipeline.config.repr.directory, pipeline.TEXT_SEARCH_INDEX_NAME, pipeline.TEXT_SEARCH_DATABASE_IDS_NAME) - progress.complete() diff --git a/winnow/text_search/__init__.py b/winnow/text_search/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/winnow/text_search/bigfile.py b/winnow/text_search/bigfile.py deleted file mode 100644 index 43378181..00000000 --- a/winnow/text_search/bigfile.py +++ /dev/null @@ -1,157 +0,0 @@ -import array -import logging -import os - -import numpy as np - -logger = logging.getLogger(__name__) - - -class BigFile: - def __init__(self, datadir, bin_file="feature.bin", hijack=None): - self.nr_of_images, self.ndims = map(int, open(os.path.join(datadir, "shape.txt")).readline().split()) - id_file = os.path.join(datadir, "id.txt") - self.names = open(id_file).read().strip().split() - assert len(self.names) == self.nr_of_images - self.name2index = dict(zip(self.names, range(self.nr_of_images))) - self.binary_file = os.path.join(datadir, bin_file) - self.hijack = hijack - logger.info( - "[%s] %dx%d instances loaded from %s" % (self.__class__.__name__, self.nr_of_images, self.ndims, datadir) - ) - - def readall(self, isname=True): - - index_name_array = [(self.name2index[x], x) for x in set(self.names) if x in self.name2index] - - index_name_array.sort(key=lambda v: v[0]) - sorted_index = [x[0] for x in index_name_array] - - nr_of_images = len(index_name_array) - offset = np.float32(1).nbytes * self.ndims - - res = array.array("f") - fr = open(self.binary_file, "rb") - fr.seek(index_name_array[0][0] * offset) - res.fromfile(fr, self.ndims) - previous = index_name_array[0][0] - - for next in sorted_index[1:]: - move = (next - 1 - previous) * offset - # print next, move - fr.seek(move, 1) - res.fromfile(fr, self.ndims) - previous = next - - fr.close() - - return [x[1] for x in index_name_array], [ - res[i * self.ndims : (i + 1) * self.ndims].tolist() for i in range(nr_of_images) - ] - - def read(self, requested, isname=True): - requested = set(requested) - if isname: - index_name_array = [(self.name2index[x], x) for x in requested if x in self.name2index] - else: - assert min(requested) >= 0 - assert max(requested) < len(self.names) - index_name_array = [(x, self.names[x]) for x in requested] - if len(index_name_array) == 0: - return [], [] - - index_name_array.sort(key=lambda v: v[0]) - sorted_index = [x[0] for x in index_name_array] - - nr_of_images = len(index_name_array) - offset = np.float32(1).nbytes * self.ndims - - res = array.array("f") - fr = open(self.binary_file, "rb") - fr.seek(index_name_array[0][0] * offset) - res.fromfile(fr, self.ndims) - previous = index_name_array[0][0] - - for next in sorted_index[1:]: - move = (next - 1 - previous) * offset - # print next, move - fr.seek(move, 1) - res.fromfile(fr, self.ndims) - previous = next - - fr.close() - - return [x[1] for x in index_name_array], [ - res[i * self.ndims : (i + 1) * self.ndims].tolist() for i in range(nr_of_images) - ] - - def read_one(self, name): - renamed, vectors = self.read([name]) - try: - if self.hijack: - return vectors[0][: self.hijack] - return vectors[0] - except Exception: - logger.exception("read_one(%s) failed", name) - - def shape(self): - - h = self.hijack - if h: - return [self.nr_of_images, self.ndims] - - return [self.nr_of_images, self.ndims] - - -class StreamFile: - def __init__(self, datadir): - self.feat_dir = datadir - self.nr_of_images, self.ndims = map(int, open(os.path.join(datadir, "shape.txt")).readline().split()) - id_file = os.path.join(datadir, "id.txt") - self.names = open(id_file).read().strip().split() - assert len(self.names) == self.nr_of_images - self.name2index = dict(zip(self.names, range(self.nr_of_images))) - self.binary_file = os.path.join(datadir, "feature.bin") - print("[%s] %dx%d instances loaded from %s" % (self.__class__.__name__, self.nr_of_images, self.ndims, datadir)) - self.fr = None - self.current = 0 - - def open(self): - self.fr = open(os.path.join(self.feat_dir, "feature.bin"), "rb") - self.current = 0 - - def close(self): - if self.fr: - self.fr.close() - self.fr = None - - def __iter__(self): - return self - - def next(self): - if self.current >= self.nr_of_images: - self.close() - raise StopIteration - else: - res = array.array("f") - res.fromfile(self.fr, self.ndims) - _id = self.names[self.current] - self.current += 1 - return _id, res.tolist() - - -if __name__ == "__main__": - feat_dir = "toydata/FeatureData/f1" - bigfile = BigFile(feat_dir) - - imset = str.split("b z a a b c") - renamed, vectors = bigfile.read(imset) - - for name, vec in zip(renamed, vectors): - print(name, vec) - - bigfile = StreamFile(feat_dir) - bigfile.open() - for name, vec in bigfile: - print(name, vec) - bigfile.close() diff --git a/winnow/text_search/build_vocab.py b/winnow/text_search/build_vocab.py deleted file mode 100644 index 12027cac..00000000 --- a/winnow/text_search/build_vocab.py +++ /dev/null @@ -1,120 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# Adapted for this project -# -------------------------------------------------------- - - -from __future__ import print_function - -import logging -import os -import pickle -import sys -from collections import Counter - -from .common import ROOT_PATH, TEXT_ENCODINGS, DEFAULT_LANG, DEFAULT_TEXT_ENCODING, MIN_WORD_COUNT -from .textlib import TextTool, Vocabulary -from .util import checkToSkip, makedirsforfile - -logger = logging.getLogger(__name__) - - -def read_from_txt_file(cap_file): - captions = [] - with open(cap_file, "rb") as fr: - for line in fr: - cap_id, caption = line.split(" ", 1) - captions.append(caption.strip()) - return captions - - -def build_vocab(cap_file, encoding, threshold, lang): - nosw = "_nsw" in encoding - logger.info("Build a simple vocabulary wrapper from %s", cap_file) - captions = read_from_txt_file(cap_file) - counter = Counter() - - for i, caption in enumerate(captions): - tokens = TextTool.tokenize(caption, language=lang, remove_stopword=nosw) - counter.update(tokens) - - # Discard if the occurrence of the word is less than min_word_cnt. - word_counts = [(word, cnt) for word, cnt in counter.items() if cnt >= threshold] - word_counts.sort(key=lambda x: x[1], reverse=True) - - # Create a vocab wrapper and add some special tokens. - vocab = Vocabulary(encoding) - if "gru" in encoding: - vocab.add("") - vocab.add("") - vocab.add("") - vocab.add("") - - # Add words to the vocabulary. - for word, c in word_counts: - vocab.add(word) - return vocab, word_counts - - -def process(options, collection): - overwrite = options.overwrite - rootpath = options.rootpath - threshold = options.threshold - encoding = options.encoding - language = options.language - - vocab_file = os.path.join(rootpath, collection, "TextData", "vocab", "%s_%d.pkl" % (encoding, threshold)) - count_file = os.path.join(os.path.dirname(vocab_file), "%s_%d.txt" % (encoding, threshold)) - - if checkToSkip(vocab_file, overwrite): - return 0 - - cap_file = os.path.join(rootpath, collection, "TextData", "%s.caption.txt" % collection) - vocab, word_counts = build_vocab(cap_file, encoding, threshold=threshold, lang=language) - - makedirsforfile(vocab_file) - with open(vocab_file, "wb") as fw: - pickle.dump(vocab, fw, pickle.HIGHEST_PROTOCOL) - logger.info("Saved vocabulary of %d words to %s", len(vocab), vocab_file) - - with open(count_file, "w") as fw: - fw.write("\n".join(["%s %d" % x for x in word_counts])) - - logger.info("Saved word-counts to %s", count_file) - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - - from optparse import OptionParser - - parser = OptionParser(usage="""usage: %prog [options] collection""") - - parser.add_option("--overwrite", default=0, type="int", help="overwrite existing file (default: 0)") - parser.add_option("--rootpath", default=ROOT_PATH, type="string", help="rootpath (default: %s)" % ROOT_PATH) - parser.add_option("--language", default=DEFAULT_LANG, type="string", help="language (default: %s)" % DEFAULT_LANG) - parser.add_option( - "--encoding", - default="bow", - type="choice", - choices=TEXT_ENCODINGS, - help="text encoding strategy. Valid choices are %s. (default: %s)" % (TEXT_ENCODINGS, DEFAULT_TEXT_ENCODING), - ) - parser.add_option( - "--threshold", default=5, type="int", help="minimum word occurrence (default: %d)" % MIN_WORD_COUNT - ) - - (options, args) = parser.parse_args(argv) - if len(args) < 1: - parser.print_help() - return 1 - - assert options.language in ["en", "zh"], "language %s not supported" % options.language - - return process(options, args[0]) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/winnow/text_search/common.py b/winnow/text_search/common.py deleted file mode 100644 index e59371d9..00000000 --- a/winnow/text_search/common.py +++ /dev/null @@ -1,19 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - -import logging -import os - -ROOT_PATH = os.path.join(os.environ["HOME"], "VisualSearch") -MIN_WORD_COUNT = 5 - -TEXT_ENCODINGS = ["bow", "bow_nsw", "gru"] -DEFAULT_TEXT_ENCODING = "bow" -DEFAULT_LANG = "en" - -logger = logging.getLogger(__file__) -logging.basicConfig( - format="[%(asctime)s - %(filename)s:line %(lineno)s] %(message)s", datefmt="%d %b %H:%M:%S", level=logging.INFO -) diff --git a/winnow/text_search/configs/__init__.py b/winnow/text_search/configs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/winnow/text_search/configs/w2vvpp_resnext101-resnet152_subspace.py b/winnow/text_search/configs/w2vvpp_resnext101-resnet152_subspace.py deleted file mode 100644 index 4643fe88..00000000 --- a/winnow/text_search/configs/w2vvpp_resnext101-resnet152_subspace.py +++ /dev/null @@ -1,42 +0,0 @@ -class config(object): - text_encoding = "bow_nsw@w2v_nsw@gru_mean" - threshold = 5 - bow_norm = 0 - we_dim = 500 - rnn_size = 1024 - rnn_layer = 1 - txt_fc_layers = "0-2048" - txt_norm = 2 # L_2 norm - use_abs = False - batch_norm = False - - vid_feat = "mean_resnext101_resnet152" - vis_fc_layers = "0-2048" - vis_norm = 2 # L_2 norm - - # dropout - dropout = 0.2 - last_dropout = 0.2 - - # activation - activation = "tanh" - last_activation = "tanh" - - # loss - loss = "mrl" - margin = 0.2 - direction = "t2i" # only valid for mrl loss - # Use max instead of sum in the rank loss - max_violation = True # only valid for mrl loss - # cost style (sum|mean) - cost_style = "sum" # only valid for mrl loss - # Similarity measure used (cosine|order) - measure = "cosine" - - # optimizer - optimizer = "rmsprop" - # Initial learning rate. - lr = 0.0001 - lr_decay_rate = 0.99 - # Gradient clipping threshold - grad_clip = 2 diff --git a/winnow/text_search/configs/w2vvpp_resnext101_subspace.py b/winnow/text_search/configs/w2vvpp_resnext101_subspace.py deleted file mode 100644 index fc7d45ce..00000000 --- a/winnow/text_search/configs/w2vvpp_resnext101_subspace.py +++ /dev/null @@ -1,42 +0,0 @@ -class config(object): - text_encoding = "bow_nsw@w2v_nsw@gru_mean" - threshold = 5 - bow_norm = 0 - we_dim = 500 - rnn_size = 1024 - rnn_layer = 1 - txt_fc_layers = "0-2048" - txt_norm = 2 # L_2 norm - use_abs = False - batch_norm = False - - vid_feat = "mean_pyresnext-101_rbps13k,flatten0_output,os" - vis_fc_layers = "0-2048" - vis_norm = 2 # L_2 norm - - # dropout - dropout = 0 - last_dropout = 0 - - # activation - activation = "tanh" - last_activation = "tanh" - - # loss - loss = "mrl" - margin = 0.2 - direction = "t2i" # only valid for mrl loss - # Use max instead of sum in the rank loss - max_violation = True # only valid for mrl loss - # cost style (sum|mean) - cost_style = "sum" # only valid for mrl loss - # Similarity measure used (cosine|order) - measure = "cosine" - - # optimizer - optimizer = "rmsprop" - # Initial learning rate. - lr = 0.0001 - lr_decay_rate = 0.99 - # Gradient clipping threshold - grad_clip = 2 diff --git a/winnow/text_search/data_provider.py b/winnow/text_search/data_provider.py deleted file mode 100644 index 1e7bd9f2..00000000 --- a/winnow/text_search/data_provider.py +++ /dev/null @@ -1,168 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# Adapted for this project -# -------------------------------------------------------- - -import torch -import torch.utils.data as data - -from .bigfile import BigFile -from .textlib import TextTool - - -def collate_vision(data): - vis_feats, idxs, vis_ids = zip(*data) - vis_feats = torch.stack(vis_feats, 0) - return vis_feats, idxs, vis_ids - - -def collate_text(data): - data.sort(key=lambda x: len(TextTool.tokenize(x[0])), reverse=True) - captions, idxs, cap_ids = zip(*data) - return captions, idxs, cap_ids - - -def collate_pair(data): - data.sort(key=lambda x: len(TextTool.tokenize(x[1])), reverse=True) - vis_feats, captions, idxs, vis_ids, cap_ids = zip(*data) - vis_feats = torch.stack(vis_feats, 0) - return vis_feats, captions, idxs, vis_ids, cap_ids - - -class VisionDataset(data.Dataset): - def __init__(self, params): - self.vis_feat_file = BigFile(params["vis_feat"]) if isinstance(params["vis_feat"], str) else params["vis_feat"] - self.vis_ids = self.vis_feat_file.names if params.get("vis_ids", None) is None else params["vis_ids"] - self.length = len(self.vis_ids) - - def __getitem__(self, index): - vis_id = self.vis_ids[index] - vis_tensor = self.get_feat_by_id(vis_id) - return vis_tensor, index, vis_id - - def get_feat_by_id(self, vis_id): - vis_tensor = torch.Tensor(self.vis_feat_file.read_one(vis_id)) - return vis_tensor - - def __len__(self): - return self.length - - -class TextDataset(data.Dataset): - def __init__(self, params): - capfile = params["capfile"] - self.captions = {} - self.cap_ids = [] - with open(capfile, "r") as reader: - for line in reader.readlines(): - cap_id, caption = line.strip().split(" ", 1) - self.captions[cap_id] = caption - self.cap_ids.append(cap_id) - - self.length = len(self.cap_ids) - - def __getitem__(self, index): - cap_id = self.cap_ids[index] - caption = self.get_caption_by_id(cap_id) - return caption, index, cap_id - - def get_caption_by_id(self, cap_id): - caption = self.captions[cap_id] - return caption - - def __len__(self): - return self.length - - -class PairDataset(data.Dataset): - def __init__(self, params): - self.visData = VisionDataset(params) - self.txtData = TextDataset(params) - - self.cap_ids = self.txtData.cap_ids - self.length = len(self.cap_ids) - - def __getitem__(self, index): - cap_id = self.cap_ids[index] - vis_id = self.get_visId_by_capId(cap_id) - - caption = self.txtData.get_caption_by_id(cap_id) - vis_feat = self.visData.get_feat_by_id(vis_id) - return vis_feat, caption, index, vis_id, cap_id - - def get_visId_by_capId(self, cap_id): - vis_id = cap_id.split("#", 1)[0] - return vis_id - - def __len__(self): - return self.length - - -def vis_provider(params): - data_loader = torch.utils.data.DataLoader( - dataset=VisionDataset(params), - batch_size=params.get("batch_size", 1), - shuffle=params.get("shuffle", False), - pin_memory=params.get("pin_memory", False), - num_workers=params.get("num_workers", 0), - collate_fn=collate_vision, - ) - return data_loader - - -def txt_provider(params): - data_loader = torch.utils.data.DataLoader( - dataset=TextDataset(params), - batch_size=params.get("batch_size", 1), - shuffle=params.get("shuffle", False), - pin_memory=params.get("pin_memory", False), - num_workers=params.get("num_workers", 0), - collate_fn=collate_text, - ) - return data_loader - - -def pair_provider(params): - - data_loader = torch.utils.data.DataLoader( - dataset=PairDataset(params), - batch_size=params.get("batch_size", 1), - shuffle=params.get("shuffle", False), - pin_memory=params.get("pin_memory", False), - num_workers=params.get("num_workers", 0), - collate_fn=collate_pair, - ) - return data_loader - - -if __name__ == "__main__": - import os - - data_path = "VisualSearch" - collection = "tgif-msrvtt10k" - vid_feat = "mean_resnext101_resnet152" - vid_feat_dir = os.path.join(collection, "FeatureData", vid_feat) - - vis_loader = vis_provider({"vis_feat": vid_feat_dir, "batch_size": 100, "num_workers": 2}) - - for i, (feat_vecs, idxs, vis_ids) in enumerate(vis_loader): - print(i, feat_vecs.shape, len(idxs)) - break - - capfile = os.path.join(collection, "TextData", "%s.caption.txt" % collection) - - txt_loader = txt_provider({"capfile": capfile, "batch_size": 100, "num_workers": 2}) - - for i, (captions, idxs, cap_ids) in enumerate(txt_loader): - print(i, captions, len(cap_ids)) - print(len(cap) for cap in captions) - break - - pair_loader = pair_provider( - {"vis_feat": vid_feat_dir, "capfile": capfile, "batch_size": 100, "num_workers": 2, "shuffle": True} - ) - for i, (vis_feats, captions, idxs, vis_ids, cap_ids) in enumerate(pair_loader): - print(i, vis_feats.shape, captions[:10], len(cap_ids)) - print(len(cap) for cap in captions) - break diff --git a/winnow/text_search/evaluation.py b/winnow/text_search/evaluation.py deleted file mode 100644 index b0822de7..00000000 --- a/winnow/text_search/evaluation.py +++ /dev/null @@ -1,162 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# Adapted for this project -# -------------------------------------------------------- - -from __future__ import print_function - -import numpy as np -import torch - -from .generic_utils import Progbar -from .util import timer - - -def l2norm(X): - """L2-normalize columns of X""" - norm = np.linalg.norm(X, axis=1, keepdims=True) - return 1.0 * X / norm - - -@timer -def cosine_sim(query_embs, retro_embs): - query_embs = l2norm(query_embs) - retro_embs = l2norm(retro_embs) - - return query_embs.dot(retro_embs.T) - - -def compute_sim(query_embs, retro_embs, measure="cosine"): - if measure == "cosine": - return cosine_sim(query_embs, retro_embs) - - elif measure == "euclidean": - raise Exception("Not implemented") - else: - raise Exception("%s is invalid" % measure) - - -def encode_data(model, data_loader): - """Encode all images and captions loadable by `data_loader`""" - model.switch_to_eval() - - vis_embs = None - txt_embs = None - vis_ids = [""] * len(data_loader.dataset) - txt_ids = [""] * len(data_loader.dataset) - - pbar = Progbar(len(data_loader.dataset)) - for i, (vis_input, txt_input, idxs, batch_vis_ids, batch_txt_ids) in enumerate(data_loader): - - with torch.no_grad(): - vis_emb = model.vis_net(vis_input) - txt_emb = model.txt_net(txt_input) - - if vis_embs is None: - vis_embs = np.zeros((len(data_loader.dataset), vis_emb.size(1))) - txt_embs = np.zeros((len(data_loader.dataset), txt_emb.size(1))) - - vis_embs[idxs] = vis_emb.data.cpu().numpy().copy() - txt_embs[idxs] = txt_emb.data.cpu().numpy().copy() - - for j, idx in enumerate(idxs): - txt_ids[idx] = batch_txt_ids[j] - vis_ids[idx] = batch_vis_ids[j] - - pbar.add(vis_emb.size(0)) - - return vis_embs, txt_embs, vis_ids, txt_ids - - -@timer -def encode_vis(model, data_loader): - model.switch_to_eval() - - vis_embs = None - vis_ids = [""] * len(data_loader.dataset) - pbar = Progbar(len(data_loader.dataset)) - for i, (vis_input, idxs, batch_vis_ids) in enumerate(data_loader): - with torch.no_grad(): - vis_emb = model.vis_net(vis_input) - - if vis_embs is None: - vis_embs = np.zeros((len(data_loader.dataset), vis_emb.size(1))) - - vis_embs[idxs] = vis_emb.data.cpu().numpy().copy() - for j, idx in enumerate(idxs): - vis_ids[idx] = batch_vis_ids[j] - - pbar.add(len(idxs)) - - return vis_embs, vis_ids - - -@timer -def encode_txt(model, data_loader): - model.switch_to_eval() - - txt_embs = None - txt_ids = [""] * len(data_loader.dataset) - pbar = Progbar(len(data_loader.dataset)) - for i, (txt_input, idxs, batch_txt_ids) in enumerate(data_loader): - with torch.no_grad(): - txt_emb = model.txt_net(txt_input) - - if txt_embs is None: - txt_embs = np.zeros((len(data_loader.dataset), txt_emb.size(1))) - - txt_embs[idxs] = txt_emb.data.cpu().numpy().copy() - for j, idx in enumerate(idxs): - txt_ids[idx] = batch_txt_ids[j] - - pbar.add(len(idxs)) - - return txt_embs, txt_ids - - -def eval_qry2retro(qry2retro_sim, n_qry=1): - """ - Query->Retrieval - qry2retro_sim: (n_qry*N, N) matrix of query to video similarity - """ - - assert qry2retro_sim.shape[0] / qry2retro_sim.shape[1] == n_qry, qry2retro_sim.shape - ranks = np.zeros(qry2retro_sim.shape[0]) - - inds = np.argsort(qry2retro_sim, axis=1) - - for index in range(len(ranks)): - ind = inds[index][::-1] - - rank = np.where(ind == index / n_qry)[0][0] - ranks[index] = rank - - # Compute metrics - r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) - r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) - r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) - medr = np.floor(np.median(ranks)) + 1 - meanr = ranks.mean() + 1 - mir = (1.0 / (ranks + 1)).mean() - - return (r1, r5, r10, medr, meanr, mir) - - -def eval(label_matrix): - ranks = np.zeros(label_matrix.shape[0]) - aps = np.zeros(label_matrix.shape[0]) - - for index in range(len(ranks)): - rank = np.where(label_matrix[index] == 1)[0] + 1 - ranks[index] = rank[0] - - aps[index] = np.mean([(i + 1.0) / rank[i] for i in range(len(rank))]) - - r1, r5, r10 = [100.0 * np.mean([x <= k for x in ranks]) for k in [1, 5, 10]] - medr = np.floor(np.median(ranks)) - meanr = ranks.mean() - mir = (1.0 / ranks).mean() - mAP = aps.mean() - - return (r1, r5, r10, medr, meanr, mir, mAP) diff --git a/winnow/text_search/generic_utils.py b/winnow/text_search/generic_utils.py deleted file mode 100644 index 22d68ed8..00000000 --- a/winnow/text_search/generic_utils.py +++ /dev/null @@ -1,434 +0,0 @@ -"""Python utilities required by Keras.""" -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# Adapted for this project -# -------------------------------------------------------- - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import binascii -import codecs -import collections -import inspect -import marshal -import sys -import time -import types as python_types - -import numpy as np -import six - -_GLOBAL_CUSTOM_OBJECTS = {} - - -class CustomObjectScope(object): - """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape. - - Code within a `with` statement will be able to access custom objects - by name. Changes to global custom objects persist - within the enclosing `with` statement. At end of the `with` statement, - global custom objects are reverted to state - at beginning of the `with` statement. - - # Example - - Consider a custom object `MyObject` (e.g. a class): - - ```python - with CustomObjectScope({'MyObject':MyObject}): - layer = Dense(..., kernel_regularizer='MyObject') - # save, load, etc. will recognize custom object by name - ``` - """ - - def __init__(self, *args): - self.custom_objects = args - self.backup = None - - def __enter__(self): - self.backup = _GLOBAL_CUSTOM_OBJECTS.copy() - for objects in self.custom_objects: - _GLOBAL_CUSTOM_OBJECTS.update(objects) - return self - - def __exit__(self, *args, **kwargs): - _GLOBAL_CUSTOM_OBJECTS.clear() - _GLOBAL_CUSTOM_OBJECTS.update(self.backup) - - -def custom_object_scope(*args): - """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape. - - Convenience wrapper for `CustomObjectScope`. - Code within a `with` statement will be able to access custom objects - by name. Changes to global custom objects persist - within the enclosing `with` statement. At end of the `with` statement, - global custom objects are reverted to state - at beginning of the `with` statement. - - # Example - - Consider a custom object `MyObject` - - ```python - with custom_object_scope({'MyObject':MyObject}): - layer = Dense(..., kernel_regularizer='MyObject') - # save, load, etc. will recognize custom object by name - ``` - - # Arguments - *args: Variable length list of dictionaries of name, - class pairs to add to custom objects. - - # Returns - Object of type `CustomObjectScope`. - """ - return CustomObjectScope(*args) - - -def get_custom_objects(): - """Retrieves a live reference to the global dictionary of custom objects. - - Updating and clearing custom objects using `custom_object_scope` - is preferred, but `get_custom_objects` can - be used to directly access `_GLOBAL_CUSTOM_OBJECTS`. - - # Example - - ```python - get_custom_objects().clear() - get_custom_objects()['MyObject'] = MyObject - ``` - - # Returns - Global dictionary of names to classes (`_GLOBAL_CUSTOM_OBJECTS`). - """ - return _GLOBAL_CUSTOM_OBJECTS - - -def serialize_keras_object(instance): - if instance is None: - return None - if hasattr(instance, "get_config"): - return {"class_name": instance.__class__.__name__, "config": instance.get_config()} - if hasattr(instance, "__name__"): - return instance.__name__ - else: - raise ValueError("Cannot serialize", instance) - - -# FIXME: simplify method -def deserialize_keras_object( # noqa: C901 - identifier, - module_objects=None, - custom_objects=None, - printable_module_name="object", -): - if isinstance(identifier, dict): - # In this case we are dealing with a Keras config dictionary. - config = identifier - if "class_name" not in config or "config" not in config: - raise ValueError("Improper config format: " + str(config)) - class_name = config["class_name"] - if custom_objects and class_name in custom_objects: - cls = custom_objects[class_name] - elif class_name in _GLOBAL_CUSTOM_OBJECTS: - cls = _GLOBAL_CUSTOM_OBJECTS[class_name] - else: - module_objects = module_objects or {} - cls = module_objects.get(class_name) - if cls is None: - raise ValueError("Unknown " + printable_module_name + ": " + class_name) - if hasattr(cls, "from_config"): - custom_objects = custom_objects or {} - if has_arg(cls.from_config, "custom_objects"): - return cls.from_config( - config["config"], - custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) + list(custom_objects.items())), - ) - with CustomObjectScope(custom_objects): - return cls.from_config(config["config"]) - else: - # Then `cls` may be a function returning a class. - # in this case by convention `config` holds - # the kwargs of the function. - custom_objects = custom_objects or {} - with CustomObjectScope(custom_objects): - return cls(**config["config"]) - elif isinstance(identifier, six.string_types): - function_name = identifier - if custom_objects and function_name in custom_objects: - fn = custom_objects.get(function_name) - elif function_name in _GLOBAL_CUSTOM_OBJECTS: - fn = _GLOBAL_CUSTOM_OBJECTS[function_name] - else: - fn = module_objects.get(function_name) - if fn is None: - raise ValueError("Unknown " + printable_module_name + ":" + function_name) - return fn - else: - raise ValueError("Could not interpret serialized " + printable_module_name + ": " + identifier) - - -def func_dump(func): - """Serializes a user defined function. - - # Arguments - func: the function to serialize. - - # Returns - A tuple `(code, defaults, closure)`. - """ - raw_code = marshal.dumps(func.__code__) - code = codecs.encode(raw_code, "base64").decode("ascii") - defaults = func.__defaults__ - if func.__closure__: - closure = tuple(c.cell_contents for c in func.__closure__) - else: - closure = None - return code, defaults, closure - - -def func_load(code, defaults=None, closure=None, globs=None): - """Deserializes a user defined function. - - # Arguments - code: bytecode of the function. - defaults: defaults of the function. - closure: closure of the function. - globs: dictionary of global objects. - - # Returns - A function object. - """ - if isinstance(code, (tuple, list)): # unpack previous dump - code, defaults, closure = code - if isinstance(defaults, list): - defaults = tuple(defaults) - - def ensure_value_to_cell(value): - """Ensures that a value is converted to a python cell object. - - # Arguments - value: Any value that needs to be casted to the cell type - - # Returns - A value wrapped as a cell object (see function "func_load") - - """ - - def dummy_fn(): - value # just access it so it gets captured in .__closure__ - - cell_value = dummy_fn.__closure__[0] - if not isinstance(value, type(cell_value)): - return cell_value - else: - return value - - if closure is not None: - closure = tuple(ensure_value_to_cell(_) for _ in closure) - try: - raw_code = codecs.decode(code.encode("ascii"), "base64") - code = marshal.loads(raw_code) - except (UnicodeEncodeError, binascii.Error, ValueError): - # backwards compatibility for models serialized prior to 2.1.2 - raw_code = code.encode("raw_unicode_escape") - code = marshal.loads(raw_code) - if globs is None: - globs = globals() - return python_types.FunctionType(code, globs, name=code.co_name, argdefs=defaults, closure=closure) - - -def has_arg(fn, name, accept_all=False): - """Checks if a callable accepts a given keyword argument. - - For Python 2, checks if there is an argument with the given name. - - For Python 3, checks if there is an argument with the given name, and - also whether this argument can be called with a keyword (i.e. if it is - not a positional-only argument). - - # Arguments - fn: Callable to inspect. - name: Check if `fn` can be called with `name` as a keyword argument. - accept_all: What to return if there is no parameter called `name` - but the function accepts a `**kwargs` argument. - - # Returns - bool, whether `fn` accepts a `name` keyword argument. - """ - if sys.version_info < (3,): - arg_spec = inspect.getargspec(fn) - if accept_all and arg_spec.keywords is not None: - return True - return name in arg_spec.args - elif sys.version_info < (3, 3): - arg_spec = inspect.getfullargspec(fn) - if accept_all and arg_spec.varkw is not None: - return True - return name in arg_spec.args or name in arg_spec.kwonlyargs - else: - signature = inspect.signature(fn) - parameter = signature.parameters.get(name) - if parameter is None: - if accept_all: - for param in signature.parameters.values(): - if param.kind == inspect.Parameter.VAR_KEYWORD: - return True - return False - return parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY) - - -class Progbar(object): - """Displays a progress bar. - - # Arguments - target: Total number of steps expected, None if unknown. - width: Progress bar width on screen. - verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose) - stateful_metrics: Iterable of string names of metrics that - should *not* be averaged over time. Metrics in this list - will be displayed as-is. All others will be averaged - by the progbar before display. - interval: Minimum visual progress update interval (in seconds). - """ - - def __init__(self, target, width=30, verbose=1, interval=0.05, stateful_metrics=None): - self.target = target - self.width = width - self.verbose = verbose - self.interval = interval - if stateful_metrics: - self.stateful_metrics = set(stateful_metrics) - else: - self.stateful_metrics = set() - - self._dynamic_display = (hasattr(sys.stdout, "isatty") and sys.stdout.isatty()) or "ipykernel" in sys.modules - self._total_width = 0 - self._seen_so_far = 0 - self._values = collections.OrderedDict() - self._start = time.time() - self._last_update = 0 - - # FIXME: simplify update function - def update(self, current, values=None): # noqa: C901 - """Updates the progress bar. - - # Arguments - current: Index of current step. - values: List of tuples: - `(name, value_for_last_step)`. - If `name` is in `stateful_metrics`, - `value_for_last_step` will be displayed as-is. - Else, an average of the metric over time will be displayed. - """ - values = values or [] - for k, v in values: - if k not in self.stateful_metrics: - if k not in self._values: - self._values[k] = [v * (current - self._seen_so_far), current - self._seen_so_far] - else: - self._values[k][0] += v * (current - self._seen_so_far) - self._values[k][1] += current - self._seen_so_far - else: - self._values[k] = v - self._seen_so_far = current - - now = time.time() - info = " - %.0fs" % (now - self._start) - if self.verbose == 1: - if now - self._last_update < self.interval and self.target is not None and current < self.target: - return - - prev_total_width = self._total_width - if self._dynamic_display: - sys.stdout.write("\b" * prev_total_width) - sys.stdout.write("\r") - else: - sys.stdout.write("\n") - - if self.target is not None: - numdigits = int(np.floor(np.log10(self.target))) + 1 - barstr = "%%%dd/%d [" % (numdigits, self.target) - bar = barstr % current - prog = float(current) / self.target - prog_width = int(self.width * prog) - if prog_width > 0: - bar += "=" * (prog_width - 1) - if current < self.target: - bar += ">" - else: - bar += "=" - bar += "." * (self.width - prog_width) - bar += "]" - else: - bar = "%7d/Unknown" % current - - self._total_width = len(bar) - sys.stdout.write(bar) - - if current: - time_per_unit = (now - self._start) / current - else: - time_per_unit = 0 - if self.target is not None and current < self.target: - eta = time_per_unit * (self.target - current) - if eta > 3600: - eta_format = "%d:%02d:%02d" % (eta // 3600, (eta % 3600) // 60, eta % 60) - elif eta > 60: - eta_format = "%d:%02d" % (eta // 60, eta % 60) - else: - eta_format = "%ds" % eta - - info = " - ETA: %s" % eta_format - else: - if time_per_unit >= 1: - info += " %.0fs/step" % time_per_unit - elif time_per_unit >= 1e-3: - info += " %.0fms/step" % (time_per_unit * 1e3) - else: - info += " %.0fus/step" % (time_per_unit * 1e6) - - for k in self._values: - info += " - %s:" % k - if isinstance(self._values[k], list): - avg = np.mean(self._values[k][0] / max(1, self._values[k][1])) - if abs(avg) > 1e-3: - info += " %.4f" % avg - else: - info += " %.4e" % avg - else: - info += " %s" % self._values[k] - - self._total_width += len(info) - if prev_total_width > self._total_width: - info += " " * (prev_total_width - self._total_width) - - if self.target is not None and current >= self.target: - info += "\n" - - sys.stdout.write(info) - sys.stdout.flush() - - elif self.verbose == 2: - if self.target is None or current >= self.target: - for k in self._values: - info += " - %s:" % k - avg = np.mean(self._values[k][0] / max(1, self._values[k][1])) - if avg > 1e-3: - info += " %.4f" % avg - else: - info += " %.4e" % avg - info += "\n" - - sys.stdout.write(info) - sys.stdout.flush() - - self._last_update = now - - def add(self, n, values=None): - self.update(self._seen_so_far + n, values) diff --git a/winnow/text_search/loss.py b/winnow/text_search/loss.py deleted file mode 100644 index 2ee0c92f..00000000 --- a/winnow/text_search/loss.py +++ /dev/null @@ -1,83 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# Adapted for this project -# -------------------------------------------------------- - -import torch -import torch.nn as nn - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def l2norm(X): - """L2-normalize columns of X""" - norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt() - X = torch.div(X, norm) - return X - - -def cosine_sim(query, retrio): - """Cosine similarity between all the query and retrio pairs""" - query, retrio = l2norm(query), l2norm(retrio) - return query.mm(retrio.t()) - - -class MarginRankingLoss(nn.Module): - """ - Compute margin ranking loss - """ - - def __init__(self, margin=0, measure="cosine", max_violation=False, cost_style="sum", direction="bidir"): - super(MarginRankingLoss, self).__init__() - self.margin = margin - self.cost_style = cost_style - self.direction = direction - if measure == "cosine": - self.sim = cosine_sim - else: - raise Exception("Not implemented.") - - self.max_violation = max_violation - - def forward(self, s, im): - # compute image-sentence score matrix - scores = self.sim(im, s) - diagonal = scores.diag().view(im.size(0), 1) - d1 = diagonal.expand_as(scores) - d2 = diagonal.t().expand_as(scores) - - # clear diagonals - I = torch.eye(scores.size(0)) > 0.5 # noqa - if torch.cuda.is_available(): - I = I.cuda() # noqa - - cost_s = None - cost_im = None - # compare every diagonal score to scores in its column - if self.direction in ["i2t", "bidir"]: - # caption retrieval - cost_s = (self.margin + scores - d1).clamp(min=0) - cost_s = cost_s.masked_fill_(I, 0) - # compare every diagonal score to scores in its row - if self.direction in ["t2i", "bidir"]: - # image retrieval - cost_im = (self.margin + scores - d2).clamp(min=0) - cost_im = cost_im.masked_fill_(I, 0) - - # keep the maximum violating negative for each query - if self.max_violation: - if cost_s is not None: - cost_s = cost_s.max(1)[0] - if cost_im is not None: - cost_im = cost_im.max(0)[0] - - if cost_s is None: - cost_s = torch.zeros(1).to(device) - if cost_im is None: - cost_im = torch.zeros(1).to(device) - - if self.cost_style == "sum": - return cost_s.sum() + cost_im.sum() - else: - return cost_s.mean() + cost_im.mean() diff --git a/winnow/text_search/main_utils.py b/winnow/text_search/main_utils.py deleted file mode 100644 index 8df47657..00000000 --- a/winnow/text_search/main_utils.py +++ /dev/null @@ -1,316 +0,0 @@ -import os -import pickle -import sys -from functools import partial -from glob import glob -from os.path import join, dirname -from pathlib import Path -from typing import Optional, Dict - -import matplotlib.pyplot as plt -import numpy as np -import torch - -import winnow.text_search.bigfile as bigfile -import winnow.text_search.configs as configs -import winnow.text_search.textlib as textlib -import winnow.text_search.txt2vec as txt2vec -from winnow.storage.remote_file_repo import RemoteFileRepo, BaseUrl -from winnow.text_search.bigfile import BigFile -from winnow.text_search.evaluation import compute_sim -from winnow.text_search.model import get_model, CrossModalNetwork -from winnow.text_search.similarity_index import SimilarityIndex -from winnow.text_search.textlib import TextTool - -# FIXME: Fix the model_best.pth.tar imports (#460) -# The model is saved using pickle with different import paths -# (e.g. `import configs` instead of import `winnow.text_search.configs`). -sys.modules["configs"] = configs -sys.modules["txt2vec"] = txt2vec -sys.modules["bigfile"] = bigfile -sys.modules["textlib"] = textlib - -MODEL_PATH = "./models/model_best.pth.tar" -W2V_PATH = "./models/vec500flickr30m.bin" - -# Default pretrained models base URL -DEFAULT_URL = "https://s3.amazonaws.com/justiceai/" - -# Default pretrained model file name -DEFAULT_MODEL_VIDEO = "model_best.pth.tar" -DEFAULT_MODEL_TEXT = "vec500flickr30m.bin" -DEFAULT_MODEL_VOCAB = "vocab.npy" - -# Default pretrained model directory -DEFAULT_DIRECTORY = join(dirname(__file__), "models/") - - -def model_artifacts(directory=None, base_url=None): - """Get remote file repository containing pretrained models.""" - directory = directory or DEFAULT_DIRECTORY - base_url = base_url or DEFAULT_URL - return RemoteFileRepo(directory=directory, remote=BaseUrl(base_url)) - - -def default_video_search_models_path(directory=None, base_url=None): - """Get default pretrained model path.""" - models = model_artifacts(directory, base_url) - - return models.get(DEFAULT_MODEL_VIDEO), models.get(DEFAULT_MODEL_TEXT), models.get(DEFAULT_MODEL_VOCAB) - - -def load_model(path_to_model=None, path_to_w2v=None) -> CrossModalNetwork: - - # with set_directory(Path(__file__).parent.resolve()) as context: - current_path = Path(os.path.curdir).resolve() - context_path = Path(__file__).parent.resolve() - - os.chdir(context_path) - - path_to_model, path_to_w2v, _ = default_video_search_models_path() - - path_to_model = os.path.abspath(path_to_model) - path_to_w2v = os.path.abspath(path_to_w2v) - - pickle.load = partial(pickle.load, encoding="latin1") - pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1") - checkpoint = torch.load( - os.path.join(path_to_model), - map_location=lambda storage, loc: storage, - pickle_module=pickle, - encoding="latin1", - ) - config = checkpoint["config"] - if hasattr(config, "t2v_w2v"): - w2v_feature_file = path_to_w2v - config.t2v_w2v.w2v.binary_file = w2v_feature_file - - model = get_model("w2vvpp")(config) - model.load_state_dict(checkpoint["model"]) - - # Resume working on original directory - os.chdir(current_path) - - return model - - -def load_search_space_bigfile(path_to_features, model): - - bigfile = BigFile(path_to_features) - videoset = bigfile.names - file_basenames, vectors = bigfile.read(videoset) - vis_vecs = np.array([model.embed_vis(x)[0].numpy() for x in vectors]) - - return file_basenames, vis_vecs - - -def show_preview(base_url, video_code): - - files = glob(os.path.join(base_url, video_code, "**.jpg")) - print(len(files), " frames found") - sample = files[:5] - img_data = [] - for fp in sample: - - im = plt.imread(fp) - img_data.append(im) - - preview = np.hstack(img_data) - plt.figure(figsize=(20, 20)) - plt.imshow(preview) - plt.show() - - -def query_bigfile(topic, file_basenames, vectors, model, base_preview_fp, preview_samples=10): - - sent_vec = model.embed_txt(topic).numpy() - ranklist = [(file_basenames[i], sim) for i, sim in enumerate(compute_sim(sent_vec, vectors, measure="cosine")[0])] - ranklist.sort(key=lambda v: v[1], reverse=True) - for i in range(preview_samples): - video_code, distance = ranklist[i] - try: - print("Query:", topic) - print("Rank:", i + 1, "Video:", video_code, "Similarity:", distance) - video_code = os.path.basename(video_code).split(".")[0] - show_preview(base_preview_fp, video_code) - except Exception as e: - print(e) - pass - - return ranklist - - -def load_search_space_from_signatures(signatures_folder, model): - - signature_files = glob(os.path.join(signatures_folder, "**/*.npy"), recursive=True) - basenames = [os.path.basename(x) for x in signature_files] - videofile_basenames = [v[:-82] for v in basenames] - signature_data = (np.load(x) for x in signature_files) - # This could potentially be refactored to use a Annoy Index (for persistence, indexing and search performance) - vis_vecs = np.array([model.embed_vis(x)[0].numpy() for x in signature_data]) - - return videofile_basenames, vis_vecs - - -def show_preview_from_frames(frames_file): - - image_data = np.load(frames_file) - sample = image_data[:5] - - preview = np.hstack(sample) - plt.figure(figsize=(20, 20)) - plt.imshow(preview) - plt.show() - - -def get_signature_activations(topic, signature_data, model=None): - - if model is None: - model = load_model() - - vis_vec = np.array([model.embed_vis(signature_data)[0].numpy()]) - - sent_vec = model.embed_txt(topic).numpy() - sims = compute_sim(sent_vec, vis_vec, measure="cosine")[0] - return sims - - -def load_vocab(path): - - return np.load(path, allow_pickle=True) - - -def get_human_readable(tokens, vocab, placeholder=""): - - in_vocab = [x if x in vocab else placeholder for x in tokens] - - return " ".join(in_vocab) - - -def get_query_context(topic, placeholder=""): - text_tool = TextTool() - artifacts = model_artifacts() - vocab = load_vocab(artifacts.get(DEFAULT_MODEL_VOCAB)) - - tokens = text_tool.tokenize(topic, remove_stopword=True) - clean_tokens = [x for x in tokens if x in vocab] - human_readable = " ".join([x if x in clean_tokens else placeholder for x in tokens]) - score = len(clean_tokens) / len(tokens) - - return dict( - original_query=topic, - tokens=tokens, - clean_tokens=clean_tokens, - human_readable=human_readable, - score=score, - ) - - -def query_signatures( - topic: str, - file_index: SimilarityIndex, - model: CrossModalNetwork = None, - min_similarity: float = 0.05, - max_count: Optional[int] = None, -): - query_data = get_query_context(topic) - sent_vec = model.embed_txt(topic).numpy()[0] - ranklist = file_index.query(sent_vec, min_similarity, max_count) - ids, similarities = zip(*ranklist) if len(ranklist) > 0 else ([], []) - return ids, similarities, query_data - - -def preview_query_results(topic, files, distances, frames_mapping, preview_samples, verbose, plot_preview): - for i in range(min(preview_samples, len(files))): - video_code, distance = files[i], distances[i] - try: - if verbose: - print("Query:", topic) - print("Rank:", i + 1, "Video:", video_code, "Similarity:", distance) - if plot_preview: - frames_file = frames_mapping[video_code] - assert os.path.exists(frames_file), "Frames file not found" - print(frames_file) - show_preview_from_frames(frames_file) - except Exception as e: - print("a", e) - pass - - -def get_frame_mapping(path_to_frames: str) -> Dict[str, str]: - """Build mapping from file name to file path.""" - assert os.path.exists(path_to_frames), "Frames Folder not found" - # get frames required for video preview - frame_files = glob(os.path.join(path_to_frames, "**")) - - assert len(frame_files) > 0, "No frames found" - - # allows quick lookup of frame files - frames_mapping = {os.path.basename(v[:-82]): v for v in frame_files} - return frames_mapping - - -class VideoSearch: - """Video Search class""" - - def __init__(self, file_index: SimilarityIndex, model: CrossModalNetwork = None): - """Instantiate a VideoSearch object - - Args: - file_index(SimilarityIndex): Index to efficiently query similar file vectors. - model (CrossModalNetwork): Text-Vis matching model. - """ - - self.model = model or load_model() - self.file_index = file_index - - def query( - self, - topic, - min_similarity: float = 0.05, - max_count: Optional[int] = 10000, - ): - files, similarity_scores, query_data = query_signatures( - topic, - self.file_index, - self.model, - min_similarity, - max_count, - ) - - return files, similarity_scores, query_data - - -def get_search_engine(path_to_signatures, path_to_frames, path_to_model=MODEL_PATH): - """High level function to get a search engine for videos (DEPRECATED) - - Args: - path_to_signatures (str): Path to a folder containing video signatures - path_to_frames (str): Path to a folder containing video signatures - path_to_model (path, optional): [description]. Defaults to MODEL_PATH. - - Returns: - [type]: [description] - """ - - # Load pre-trained model - model = load_model(path_to_model) - - # Load search embeddings (implement load from local storage if available) - video_file_basenames, vis_vecs = load_search_space_from_signatures(path_to_signatures, model) - - # get frames required for video preview - frames = glob(os.path.join(path_to_frames, "**")) - # allows quick lookup of frames file - frames_mapping = {os.path.basename(v[:-82]): v for v in frames} - - # instantiates the search engine - search_engine = partial( - query_signatures, - file_basenames=video_file_basenames, - vectors=vis_vecs, - model=model, - frames_mapping=frames_mapping, - ) - - return search_engine diff --git a/winnow/text_search/model.py b/winnow/text_search/model.py deleted file mode 100644 index c46f3979..00000000 --- a/winnow/text_search/model.py +++ /dev/null @@ -1,404 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - - -from collections import OrderedDict - -import numpy as np -import torch -import torch.backends.cudnn as cudnn -import torch.nn as nn -import torch.nn.init -from torch.nn.utils.clip_grad import clip_grad_norm_ -from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence - -from .bigfile import BigFile -from .loss import MarginRankingLoss - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def get_we(vocab, w2v_dir): - w2v = BigFile(w2v_dir) - ndims = w2v.ndims - nr_words = len(vocab) - words = [vocab[i] for i in range(nr_words)] - we = np.random.uniform(low=-1.0, high=1.0, size=(nr_words, ndims)) - - renamed, vecs = w2v.read(words) - for i, word in enumerate(renamed): - idx = vocab.find(word) - we[idx] = vecs[i] - - return torch.Tensor(we) - - -def l2norm(X): - """L2-normalize columns of X""" - norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt() - X = torch.div(X, norm) - return X - - -def _initialize_weights(m): - """Initialize module weights""" - if type(m) == nn.Linear: - nn.init.xavier_uniform_(m.weight) - if m.bias is not None: - nn.init.zeros_(m.bias) - elif type(m) == nn.BatchNorm1d: - nn.init.ones_(m.weight) - nn.init.zeros_(m.bias) - - -class IdentityNet(nn.Module): - def __init__(self, opt): - super(IdentityNet, self).__init__() - - def forward(self, input_x): - """Extract image feature vectors.""" - return input_x - - -class TransformNet(nn.Module): - def __init__(self, fc_layers, opt): - super(TransformNet, self).__init__() - - self.fc1 = nn.Linear(fc_layers[0], fc_layers[1]) - if opt.batch_norm: - self.bn1 = nn.BatchNorm1d(fc_layers[1]) - else: - self.bn1 = None - - if opt.activation == "tanh": - self.activation = nn.Tanh() - elif opt.activation == "relu": - self.activation = nn.ReLU() - else: - self.activation = None - - if opt.dropout > 1e-3: - self.dropout = nn.Dropout(p=opt.dropout) - else: - self.dropout = None - - self.init_weights() - - def init_weights(self): - """Xavier initialization for the fully connected layer""" - self.apply(_initialize_weights) - - def forward(self, input_x): - features = self.fc1(input_x.to(device)) - - if self.bn1 is not None: - features = self.bn1(features) - - if self.activation is not None: - features = self.activation(features) - - if self.dropout is not None: - features = self.dropout(features) - - return features - - def load_state_dict(self, state_dict): - """Copies parameters. overwritting the default one to - accept state_dict from Full model - """ - own_state = self.state_dict() - new_state = OrderedDict() - for name, param in state_dict.items(): - if name in own_state: - new_state[name] = param - - super(TransformNet, self).load_state_dict(new_state) - - -class VisTransformNet(TransformNet): - def __init__(self, opt): - super(VisTransformNet, self).__init__(opt.vis_fc_layers, opt) - - -class TxtTransformNet(TransformNet): - def __init__(self, opt): - super(TxtTransformNet, self).__init__(opt.txt_fc_layers, opt) - - -class TxtEncoder(nn.Module): - def __init__(self, opt): - super(TxtEncoder, self).__init__() - - def forward(self, txt_input): - return txt_input - - -class GruTxtEncoder(TxtEncoder): - def _init_rnn(self, opt): - self.rnn = nn.GRU(opt.we_dim, opt.rnn_size, opt.rnn_layer, batch_first=True) - - def __init__(self, opt): - super(GruTxtEncoder, self).__init__(opt) - self.pooling = opt.pooling - self.rnn_size = opt.rnn_size - self.t2v_idx = opt.t2v_idx - self.we = nn.Embedding(len(self.t2v_idx.vocab), opt.we_dim) - if opt.we_dim == 500: - self.we.weight = nn.Parameter(opt.we) # initialize with a pre-trained 500-dim w2v - - self._init_rnn(opt) - - def forward(self, txt_input): - """Handles variable size captions""" - batch_size = len(txt_input) - - # caption encoding - idx_vecs = [self.t2v_idx.encoding(caption) for caption in txt_input] - lengths = [len(vec) for vec in idx_vecs] - - x = torch.zeros(batch_size, max(lengths)).long().to(device) - for i, vec in enumerate(idx_vecs): - end = lengths[i] - x[i, :end] = torch.Tensor(vec) - - # caption embedding - x = self.we(x) - packed = pack_padded_sequence(x, lengths, batch_first=True) - - # Forward propagate RNN - out, _ = self.rnn(packed) - # Reshape *final* output to (batch_size, hidden_size) - padded = pad_packed_sequence(out, batch_first=True) - - if self.pooling == "mean": - out = torch.zeros(batch_size, self.rnn_size).to(device) - for i, ln in enumerate(lengths): - out[i] = torch.mean(padded[0][i][:ln], dim=0) - elif self.pooling == "last": - I = torch.LongTensor(lengths).view(-1, 1, 1) # noqa: E741 - I = I.expand(batch_size, 1, self.rnn_size) - 1 # noqa: E741 - I = I.cuda() # noqa: E741 - out = torch.gather(padded[0], 1, I).squeeze(1) - elif self.rnn_type == "mean_last": - out1 = torch.zeros(batch_size, self.rnn_size).to(device) - for i, ln in enumerate(lengths): - out1[i] = torch.mean(padded[0][i][:ln], dim=0) - - I = torch.LongTensor(lengths).view(-1, 1, 1) # noqa: E741 - I = I.expand(batch_size, 1, self.rnn_size) - 1 # noqa: E741 - I = I.cuda() # noqa: E741 - out2 = torch.gather(padded[0], 1, I).squeeze(1) - out = torch.cat((out1, out2), dim=1) - return out - - -class BoWTxtEncoder(TxtEncoder): - def __init__(self, opt): - super(BoWTxtEncoder, self).__init__(opt) - self.t2v_bow = opt.t2v_bow - - def forward(self, txt_input): - bow_out = torch.Tensor([self.t2v_bow.encoding(caption) for caption in txt_input]).to(device) - return bow_out - - -class W2VTxtEncoder(TxtEncoder): - def __init__(self, opt): - super(W2VTxtEncoder, self).__init__(opt) - self.t2v_w2v = opt.t2v_w2v - - def forward(self, txt_input): - w2v_out = torch.Tensor([self.t2v_w2v.encoding(caption) for caption in txt_input]).to(device) - return w2v_out - - -class MultiScaleTxtEncoder(TxtEncoder): - def __init__(self, opt): - super(MultiScaleTxtEncoder, self).__init__(opt) - self.rnn_encoder = GruTxtEncoder(opt) - self.w2v_encoder = W2VTxtEncoder(opt) - self.bow_encoder = BoWTxtEncoder(opt) - - def forward(self, txt_input): - """Handles variable size captions""" - # Embed word ids to vectors - rnn_out = self.rnn_encoder(txt_input) - w2v_out = self.w2v_encoder(txt_input) - bow_out = self.bow_encoder(txt_input) - out = torch.cat((rnn_out, w2v_out, bow_out), dim=1) - return out - - -class TxtNet(nn.Module): - def _init_encoder(self, opt): - self.encoder = TxtEncoder(opt) - - def _init_transformer(self, opt): - self.transformer = TxtTransformNet(opt) - - def __init__(self, opt): - super(TxtNet, self).__init__() - self._init_encoder(opt) - self._init_transformer(opt) - - def forward(self, txt_input): - features = self.encoder(txt_input) - features = self.transformer(features) - return features - - -class MultiScaleTxtNet(TxtNet): - def _init_encoder(self, opt): - self.encoder = MultiScaleTxtEncoder(opt) - - -class CrossModalNetwork(object): - def _init_vis_net(self, opt): - # FIXME: undefined symbol VisNet - self.vis_net = VisNet(opt) # noqa: F821 - - def _init_txt_net(self, opt): - self.txt_net = TxtNet(opt) - - def __init__(self, opt): - self._init_vis_net(opt) - self._init_txt_net(opt) - - self.grad_clip = opt.grad_clip - if torch.cuda.is_available(): - self.vis_net.cuda() - self.txt_net.cuda() - cudnn.benchmark = True - - self.criterion = MarginRankingLoss( - margin=opt.margin, - measure=opt.measure, - max_violation=opt.max_violation, - cost_style=opt.cost_style, - direction=opt.direction, - ) - - params = list(self.vis_net.parameters()) - params += list(self.txt_net.parameters()) - self.params = params - - if opt.optimizer == "adam": - self.optimizer = torch.optim.Adam(params, lr=opt.lr) - elif opt.optimizer == "rmsprop": - self.optimizer = torch.optim.RMSprop(params, lr=opt.lr) - - self.lr_schedulers = [ - torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=opt.lr_decay_rate), - torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, mode="max", factor=0.5, patience=2), - ] - - self.iters = 0 - - def state_dict(self): - state_dict = [self.vis_net.state_dict(), self.txt_net.state_dict()] - return state_dict - - def load_state_dict(self, state_dict): - self.vis_net.load_state_dict(state_dict[0]) - self.txt_net.load_state_dict(state_dict[1]) - - def switch_to_train(self): - self.vis_net.train() - self.txt_net.train() - - def switch_to_eval(self): - self.vis_net.eval() - self.txt_net.eval() - - @property - def learning_rate(self): - """Return learning rate""" - lr_list = [] - for param_group in self.optimizer.param_groups: - lr_list.append(param_group["lr"]) - return lr_list - - def lr_step(self, val_value): - self.lr_schedulers[0].step() - self.lr_schedulers[1].step(val_value) - - def compute_loss(self, vis_embs, txt_embs): - """Compute the loss given pairs of image and caption embeddings""" - loss = self.criterion(txt_embs, vis_embs) - return loss - - def train(self, vis_input, txt_input): - """One training step given vis_feats and captions.""" - self.iters += 1 - - # compute the embeddings - vis_embs = self.vis_net(vis_input) - txt_embs = self.txt_net(txt_input) - - # measure accuracy and record loss - self.optimizer.zero_grad() - loss = self.compute_loss(vis_embs, txt_embs) - - loss_value = loss.item() - - # compute gradient and do SGD step - loss.backward() - if self.grad_clip > 0: - clip_grad_norm_(self.params, self.grad_clip) - self.optimizer.step() - - return loss_value - - def embed_vis(self, vis_input): - self.switch_to_eval() - vis_input = np.array(vis_input) - if vis_input.ndim == 1: - vis_input = [vis_input] - - with torch.no_grad(): - vis_input = torch.Tensor(vis_input).to(device) - vis_embs = self.vis_net(vis_input) - - return vis_embs.cpu() - - def embed_txt(self, txt_input): - self.switch_to_eval() - if isinstance(txt_input, str): - txt_input = [txt_input] - - with torch.no_grad(): - txt_embs = self.txt_net(txt_input) - - return txt_embs.cpu() - - -""" -class W2VV (CrossModalNetwork): - def __init_vis_net(self, opt): - self.vis_net = IdentityNet(opt) - - def __init_txt_net(self, opt): - self.txt_net = MultiScaleTxtNet(opt) -""" - - -class W2VVPP(CrossModalNetwork): - def _init_vis_net(self, opt): - self.vis_net = VisTransformNet(opt) - - def _init_txt_net(self, opt): - self.txt_net = MultiScaleTxtNet(opt) - - -NAME_TO_MODELS = {"w2vvpp": W2VVPP} - - -def get_model(name): - assert name in NAME_TO_MODELS, "%s not supported." % name - return NAME_TO_MODELS[name] - - -if __name__ == "__main__": - model = get_model("w2vvpp") diff --git a/winnow/text_search/similarity_index.py b/winnow/text_search/similarity_index.py deleted file mode 100644 index f2f1a214..00000000 --- a/winnow/text_search/similarity_index.py +++ /dev/null @@ -1,229 +0,0 @@ -import abc -import logging -import math -import os.path -import pickle -from typing import Optional, List, Tuple, Any, Union - -import numpy as np -from annoy import AnnoyIndex -from sklearn.neighbors import KDTree, BallTree - -from winnow.text_search.evaluation import cosine_sim, l2norm -from winnow.utils.logging import logger_name - - -class SimilarityIndex(abc.ABC): - """API for a data-structure to query cosine-similar vectors.""" - - @abc.abstractmethod - def query( - self, - vector: np.ndarray, - min_similarity: float, - max_items: Optional[int] = None, - ) -> List[Tuple[Any, float]]: - """Query similar vectors and return (id, distance) pairs.""" - - -# Type Hint for array-like objects -ArrayLike = Union[List[Any], np.ndarray] - - -class LinearSimilarityIndex(SimilarityIndex): - """Find similar entries by considering all vectors from the collection one by one.""" - - def __init__(self, ids: ArrayLike, vectors: np.ndarray): - """ - Args: - ids (np.ndarray): Array-like collection of shape (n_samples,) containing data ids. - vectors (np.ndarray): Array-like collection of shape (n_samples, n_features) containing data vectors. - """ - self._ids = np.array(ids) - self._vectors = vectors - self._logger = logging.getLogger(logger_name(self)) - if len(self._ids) != len(self._vectors): - raise ValueError(f"Ids count did not match vectors count: {len(self._ids)} != {len(self._vectors)}") - - def query( - self, - vector: np.ndarray, - min_similarity: float, - max_items: Optional[int] = None, - ) -> List[Tuple[Any, float]]: - """Get all similar enough vectors from the index.""" - similarity = cosine_sim(np.array([vector]), self._vectors)[0] - similarity_index = [(sim, index) for index, sim in enumerate(similarity) if sim >= min_similarity] - similarity_index.sort(reverse=True) - if max_items is not None and len(similarity_index) > max_items: - similarity_index = similarity_index[:max_items] - return [(self._ids[index], sim) for sim, index in similarity_index] - - def save(self, directory: str, index_name: str): - """Save index to the file system.""" - ids_path, vectors_path = self._file_paths(directory, index_name) - self._logger.info("Saving LinearSimilarityIndex with %s items to %s", len(self._vectors), vectors_path) - np.save(ids_path, self._ids) - np.save(vectors_path, self._vectors) - self._logger.info("Saved LinearSimilarityIndex with %s items", len(self._vectors)) - - @staticmethod - def _file_paths(directory: str, index_name: str) -> Tuple[str, str]: - """Get file paths for ids and vectors.""" - common_prefix = os.path.join(os.path.abspath(directory), index_name) - return f"{common_prefix}.ids.npy", f"{common_prefix}.vectors.npy" - - @staticmethod - def load(directory: str, index_name: str, allow_pickle: bool = False) -> "LinearSimilarityIndex": - """Load linear index from the file system.""" - logger = logging.getLogger(logger_name(LinearSimilarityIndex)) - ids_path, vectors_path = LinearSimilarityIndex._file_paths(directory, index_name) - logger.info("Loading LinearSimilarityIndex from %s", vectors_path) - ids = np.load(ids_path, allow_pickle=allow_pickle) - vectors = np.load(vectors_path) - result = LinearSimilarityIndex(ids, vectors) - logger.info("Loaded LinearSimilarityIndex with %s vectors", len(vectors)) - return result - - -# Type Hint for search tree -SearchTree = Union[KDTree, BallTree] - - -class SearchTreeIndex(SimilarityIndex): - def __init__(self, ids: ArrayLike, search_tree: SearchTree, n_dimensions: int): - self._ids: ArrayLike = ids - self._search_tree: SearchTree = search_tree - self._n_dimensions: int = n_dimensions - - def query( - self, - vector: np.ndarray, - min_similarity: float, - max_items: Optional[int] = None, - ) -> List[Tuple[Any, float]]: - radius = math.sqrt(2.0 * (1 - min_similarity) / self._n_dimensions) - query_vectors = l2norm(np.array([vector])) - indices, distances = self._search_tree.query_radius(query_vectors, radius, return_distance=True) - return list(zip(self._ids[indices], distances)) - - @staticmethod - def _file_path(index_name, output_directory) -> str: - """Get index file path.""" - return os.path.join(output_directory, f"{index_name}.pickle") - - def save(self, output_directory: str, index_name: str): - """Save SearchTreeIndex to the file system.""" - path = self._file_path(index_name, output_directory) - with open(path, "wb") as file: - pickle.dump(self, file, protocol=4) - - @staticmethod - def load(output_directory: str, index_name: str) -> "SearchTreeIndex": - """Load search tree from the file system.""" - path = SearchTreeIndex._file_path(output_directory, index_name) - with open(path, "rb") as file: - return pickle.load(file) - - -class AnnoySimilarityIndex(SimilarityIndex): - def __init__(self): - self._ids: Optional[np.ndarray] = None - self._annoy_index: Optional[AnnoyIndex] = None - - def _ensure_fit(self): - """Ensure index is initialized.""" - if self._ids is None or self._annoy_index is None: - raise RuntimeError("Index is not initialized.") - - def fit(self, ids, vectors, n_trees: int = 10) -> "AnnoySimilarityIndex": - self._ids = ids - - n_features = vectors.shape[1] - self._annoy_index = AnnoyIndex(f=n_features, metric="angular") - for i, vector in enumerate(vectors): - self._annoy_index.add_item(i, vector) - self._annoy_index.build(n_trees=n_trees) - return self - - def query( - self, - vector: np.ndarray, - min_similarity: float, - max_items: int = 10000, - ) -> List[Tuple[Any, float]]: - self._ensure_fit() - indices, distances = self._annoy_index.get_nns_by_vector(vector, n=max_items, include_distances=True) - - results = [] - for index, angular_distance in zip(indices, distances): - similarity = self._similarity(angular_distance) - if similarity > min_similarity: - results.append((self._ids[index], similarity)) - - return results # Already sorted - - @staticmethod - def _file_paths(directory: str, index_name: str, ids_name: Optional[str] = None) -> Tuple[str, str]: - """Get file names for ids and vectors.""" - ids_name = ids_name or index_name - ids_path = f"{os.path.join(os.path.abspath(directory), ids_name)}.ids.npy" - index_path = f"{os.path.join(os.path.abspath(directory), index_name)}.index.ann" - return ids_path, index_path - - def save(self, output_directory: str, index_name: str, ids_name: Optional[str] = None): - """Save index to the file system.""" - self._ensure_fit() - ids_path, index_path = self._file_paths(output_directory, index_name, ids_name) - logger = logging.getLogger(logger_name(AnnoySimilarityIndex)) - logger.info("Saving AnnoySimilarityIndex with %s items to %s", self._annoy_index.get_n_items(), index_path) - np.save(ids_path, self._ids) - self._annoy_index.save(index_path) - logger.info("Saved AnnoySimilarityIndex") - - def load( - self, - directory: str, - index_name: str, - n_features: int, - ids_name: Optional[str] = None, - allow_pickle: bool = False, - ) -> "AnnoySimilarityIndex": - """Load linear index from the file system.""" - if self._ids is not None or self._annoy_index is not None: - raise RuntimeError("Already initialized.") - logger = logging.getLogger(logger_name(AnnoySimilarityIndex)) - ids_path, index_path = AnnoySimilarityIndex._file_paths(directory, index_name, ids_name) - if not os.path.exists(ids_path): - raise FileNotFoundError(f"Semantic text search IDs-index not found: {ids_path}") - if not os.path.exists(index_path): - raise FileNotFoundError(f"Semantic text search annoy index not found: {index_path}") - logger.info("Loading similarity index from %s", index_path) - self._annoy_index = AnnoyIndex(f=n_features, metric="angular") - self._annoy_index.load(index_path) - logger.info("Loading similarity index ids from %s", ids_path) - self._ids = np.load(ids_path, allow_pickle=allow_pickle) - if len(self._ids) != self._annoy_index.get_n_items(): - raise ValueError( - "The number of ids doesn't match number of indexed vectors:" - f" {len(self._ids)} != {self._annoy_index.get_n_items()}" - ) - return self - - @staticmethod - def _similarity(angular_distance: float) -> float: - """ - Reverse Annoy's angular distance to cosine similarity. - - According to Annoy documentation, angular distance is calculated - as sqrt(2(1-cos(u,v))) for normalized vectors u, v. This method - reversed distance back to the cosine similarity (i.e. cos(u,v)). - See: https://github.com/spotify/annoy#full-python-api - """ - return 1.0 - (angular_distance ** 2) / 2.0 - - @staticmethod - def exists(directory: str, index_name: str, ids_name: Optional[str] = None) -> bool: - """Check if the given index exists in the file system.""" - ids_path, index_path = AnnoySimilarityIndex._file_paths(directory, index_name, ids_name) - return os.path.exists(ids_path) and os.path.exists(index_path) diff --git a/winnow/text_search/stopwords_en.txt b/winnow/text_search/stopwords_en.txt deleted file mode 100644 index b43bdbf1..00000000 --- a/winnow/text_search/stopwords_en.txt +++ /dev/null @@ -1,153 +0,0 @@ -i -me -my -myself -we -our -ours -ourselves -you -your -yours -yourself -yourselves -he -him -his -himself -she -her -hers -herself -it -its -itself -they -them -their -theirs -themselves -what -which -who -whom -this -that -these -those -am -is -are -was -were -be -been -being -have -has -had -having -do -does -did -doing -a -an -the -and -but -if -or -because -as -until -while -of -at -by -for -with -about -against -between -into -through -during -before -after -above -below -to -from -up -down -in -out -on -off -over -under -again -further -then -once -here -there -when -where -why -how -all -any -both -each -few -more -most -other -some -such -no -nor -not -only -own -same -so -than -too -very -s -t -can -will -just -don -should -now -d -ll -m -o -re -ve -y -ain -aren -couldn -didn -doesn -hadn -hasn -haven -isn -ma -mightn -mustn -needn -shan -shouldn -wasn -weren -won -wouldn \ No newline at end of file diff --git a/winnow/text_search/stopwords_zh.txt b/winnow/text_search/stopwords_zh.txt deleted file mode 100644 index 7054330e..00000000 --- a/winnow/text_search/stopwords_zh.txt +++ /dev/null @@ -1,3332 +0,0 @@ --- -? -“ -” -》 -－－ -一 -一下 -一些 -一切 -一则 -一天 -一定 -一方面 -一旦 -一时 -一来 -一样 -一次 -一片 -一直 -一致 -一般 -一起 -一边 -一面 -万一 -上下 -上升 -上去 -上来 -上述 -上面 -下列 -下去 -下来 -下面 -不一 -不久 -不仅 -不会 -不但 -不光 -不单 -不变 -不只 -不可 -不同 -不够 -不如 -不得 -不怕 -不惟 -不成 -不拘 -不敢 -不断 -不是 -不比 -不然 -不特 -不独 -不管 -不能 -不要 -不论 -不足 -不过 -不问 -与 -与其 -与否 -与此同时 -专门 -且 -两者 -严格 -严重 -个 -个人 -个别 -中小 -中间 -丰富 -临 -为 -为主 -为了 -为什么 -为什麽 -为何 -为着 -主张 -主要 -举行 -乃 -乃至 -么 -之 -之一 -之前 -之后 -之後 -之所以 -之类 -乌乎 -乎 -乘 -也 -也好 -也是 -也罢 -了 -了解 -争取 -于 -于是 -于是乎 -云云 -互相 -产生 -人们 -人家 -什么 -什么样 -什麽 -今后 -今天 -今年 -今後 -仍然 -从 -从事 -从而 -他 -他人 -他们 -他的 -代替 -以 -以上 -以下 -以为 -以便 -以免 -以前 -以及 -以后 -以外 -以後 -以来 -以至 -以至于 -以致 -们 -任 -任何 -任凭 -任务 -企图 -伟大 -似乎 -似的 -但 -但是 -何 -何况 -何处 -何时 -作为 -你 -你们 -你的 -使得 -使用 -例如 -依 -依照 -依靠 -促进 -保持 -俺 -俺们 -倘 -倘使 -倘或 -倘然 -倘若 -假使 -假如 -假若 -做到 -像 -允许 -充分 -先后 -先後 -先生 -全部 -全面 -兮 -共同 -关于 -其 -其一 -其中 -其二 -其他 -其余 -其它 -其实 -其次 -具体 -具体地说 -具体说来 -具有 -再者 -再说 -冒 -冲 -决定 -况且 -准备 -几 -几乎 -几时 -凭 -凭借 -出去 -出来 -出现 -分别 -则 -别 -别的 -别说 -到 -前后 -前者 -前进 -前面 -加之 -加以 -加入 -加强 -十分 -即 -即令 -即使 -即便 -即或 -即若 -却不 -原来 -又 -及 -及其 -及时 -及至 -双方 -反之 -反应 -反映 -反过来 -反过来说 -取得 -受到 -变成 -另 -另一方面 -另外 -只是 -只有 -只要 -只限 -叫 -叫做 -召开 -叮咚 -可 -可以 -可是 -可能 -可见 -各 -各个 -各人 -各位 -各地 -各种 -各级 -各自 -合理 -同 -同一 -同时 -同样 -后来 -后面 -向 -向着 -吓 -吗 -否则 -吧 -吧哒 -吱 -呀 -呃 -呕 -呗 -呜 -呜呼 -呢 -周围 -呵 -呸 -呼哧 -咋 -和 -咚 -咦 -咱 -咱们 -咳 -哇 -哈 -哈哈 -哉 -哎 -哎呀 -哎哟 -哗 -哟 -哦 -哩 -哪 -哪个 -哪些 -哪儿 -哪天 -哪年 -哪怕 -哪样 -哪边 -哪里 -哼 -哼唷 -唉 -啊 -啐 -啥 -啦 -啪达 -喂 -喏 -喔唷 -嗡嗡 -嗬 -嗯 -嗳 -嘎 -嘎登 -嘘 -嘛 -嘻 -嘿 -因 -因为 -因此 -因而 -固然 -在 -在下 -地 -坚决 -坚持 -基本 -处理 -复杂 -多 -多少 -多数 -多次 -大力 -大多数 -大大 -大家 -大批 -大约 -大量 -失去 -她 -她们 -她的 -好的 -好象 -如 -如上所述 -如下 -如何 -如其 -如果 -如此 -如若 -存在 -宁 -宁可 -宁愿 -宁肯 -它 -它们 -它们的 -它的 -安全 -完全 -完成 -实现 -实际 -宣布 -容易 -密切 -对 -对于 -对应 -将 -少数 -尔后 -尚且 -尤其 -就 -就是 -就是说 -尽 -尽管 -属于 -岂但 -左右 -巨大 -巩固 -己 -已经 -帮助 -常常 -并 -并不 -并不是 -并且 -并没有 -广大 -广泛 -应当 -应用 -应该 -开外 -开始 -开展 -引起 -强烈 -强调 -归 -当 -当前 -当时 -当然 -当着 -形成 -彻底 -彼 -彼此 -往 -往往 -待 -後来 -後面 -得 -得出 -得到 -心里 -必然 -必要 -必须 -怎 -怎么 -怎么办 -怎么样 -怎样 -怎麽 -总之 -总是 -总的来看 -总的来说 -总的说来 -总结 -总而言之 -恰恰相反 -您 -意思 -愿意 -慢说 -成为 -我 -我们 -我的 -或 -或是 -或者 -战斗 -所 -所以 -所有 -所谓 -打 -扩大 -把 -抑或 -拿 -按 -按照 -换句话说 -换言之 -据 -掌握 -接着 -接著 -故 -故此 -整个 -方便 -方面 -旁人 -无宁 -无法 -无论 -既 -既是 -既然 -时候 -明显 -明确 -是 -是否 -是的 -显然 -显著 -普通 -普遍 -更加 -曾经 -替 -最后 -最大 -最好 -最後 -最近 -最高 -有 -有些 -有关 -有利 -有力 -有所 -有效 -有时 -有点 -有的 -有着 -有著 -望 -朝 -朝着 -本 -本着 -来 -来着 -极了 -构成 -果然 -果真 -某 -某个 -某些 -根据 -根本 -欢迎 -正在 -正如 -正常 -此 -此外 -此时 -此间 -毋宁 -每 -每个 -每天 -每年 -每当 -比 -比如 -比方 -比较 -毫不 -没有 -沿 -沿着 -注意 -深入 -清楚 -满足 -漫说 -焉 -然则 -然后 -然後 -然而 -照 -照着 -特别是 -特殊 -特点 -现代 -现在 -甚么 -甚而 -甚至 -用 -由 -由于 -由此可见 -的 -的话 -目前 -直到 -直接 -相似 -相信 -相反 -相同 -相对 -相对而言 -相应 -相当 -相等 -省得 -看出 -看到 -看来 -看看 -看见 -真是 -真正 -着 -着呢 -矣 -知道 -确定 -离 -积极 -移动 -突出 -突然 -立即 -第 -等 -等等 -管 -紧接着 -纵 -纵令 -纵使 -纵然 -练习 -组成 -经 -经常 -经过 -结合 -结果 -给 -绝对 -继续 -继而 -维持 -综上所述 -罢了 -考虑 -者 -而 -而且 -而况 -而外 -而已 -而是 -而言 -联系 -能 -能否 -能够 -腾 -自 -自个儿 -自从 -自各儿 -自家 -自己 -自身 -至 -至于 -良好 -若 -若是 -若非 -范围 -莫若 -获得 -虽 -虽则 -虽然 -虽说 -行为 -行动 -表明 -表示 -被 -要 -要不 -要不是 -要不然 -要么 -要是 -要求 -规定 -觉得 -认为 -认真 -认识 -让 -许多 -论 -设使 -设若 -该 -说明 -诸位 -谁 -谁知 -赶 -起 -起来 -起见 -趁 -趁着 -越是 -跟 -转动 -转变 -转贴 -较 -较之 -边 -达到 -迅速 -过 -过去 -过来 -运用 -还是 -还有 -这 -这个 -这么 -这么些 -这么样 -这么点儿 -这些 -这会儿 -这儿 -这就是说 -这时 -这样 -这点 -这种 -这边 -这里 -这麽 -进入 -进步 -进而 -进行 -连 -连同 -适应 -适当 -适用 -逐步 -逐渐 -通常 -通过 -造成 -遇到 -遭到 -避免 -那 -那个 -那么 -那么些 -那么样 -那些 -那会儿 -那儿 -那时 -那样 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-； -？ -． -, -． -' -? -· -——— -── -? -— -< -> -（ -） -〔 -〕 -[ -] -( -) -- -+ -～ -× -／ -/ -① -② -③ -④ -⑤ -⑥ -⑦ -⑧ -⑨ -⑩ -Ⅲ -В -" -; -# -@ -γ -μ -φ -φ． -× -Δ -■ -▲ -＃ -％ -＆ -＇ -＋ -＋ξ -＋＋ -－ -－β -＜ -＜± -＜Δ -＜λ -＜φ -＜＜ -= -＝ -＝☆ -＝－ -＞ -＞λ -＿ -～± -～＋ -［⑤ｆ］ -［⑤ｄ］ -［②ｉ］ -≈ -［②Ｇ］ -［①ｆ］ -ＬＩ -㈧ -［－ -...... -〉 -［③⑩］ -第二 -一番 -一直 -一个 -一些 -许多 -种 -有的是 -也就是说 -末##末 -啊 -阿 -哎 -哎呀 -哎哟 -唉 -俺 -俺们 -按 -按照 -吧 -吧哒 -把 -罢了 -被 -本 -本着 -比 -比方 -比如 -鄙人 -彼 -彼此 -边 -别 -别的 -别说 -并 -并且 -不比 -不成 -不单 -不但 -不独 -不管 -不光 -不过 -不仅 -不拘 -不论 -不怕 -不然 -不如 -不特 -不惟 -不问 -不只 -朝 -朝着 -趁 -趁着 -乘 -冲 -除 -除此之外 -除非 -除了 -此 -此间 -此外 -从 -从而 -打 -待 -但 -但是 -当 -当着 -到 -得 -的 -的话 -等 -等等 -地 -第 -叮咚 -对 -对于 -多 -多少 -而 -而况 -而且 -而是 -而外 -而言 -而已 -尔后 -反过来 -反过来说 -反之 -非但 -非徒 -否则 -嘎 -嘎登 -该 -赶 -个 -各 -各个 -各位 -各种 -各自 -给 -根据 -跟 -故 -故此 -固然 -关于 -管 -归 -果然 -果真 -过 -哈 -哈哈 -呵 -和 -何 -何处 -何况 -何时 -嘿 -哼 -哼唷 -呼哧 -乎 -哗 -还是 -还有 -换句话说 -换言之 -或 -或是 -或者 -极了 -及 -及其 -及至 -即 -即便 -即或 -即令 -即若 -即使 -几 -几时 -己 -既 -既然 -既是 -继而 -加之 -假如 -假若 -假使 -鉴于 -将 -较 -较之 -叫 -接着 -结果 -借 -紧接着 -进而 -尽 -尽管 -经 -经过 -就 -就是 -就是说 -据 -具体地说 -具体说来 -开始 -开外 -靠 -咳 -可 -可见 -可是 -可以 -况且 -啦 -来 -来着 -离 -例如 -哩 -连 -连同 -两者 -了 -临 -另 -另外 -另一方面 -论 -嘛 -吗 -慢说 -漫说 -冒 -么 -每 -每当 -们 -莫若 -某 -某个 -某些 -拿 -哪 -哪边 -哪儿 -哪个 -哪里 -哪年 -哪怕 -哪天 -哪些 -哪样 -那 -那边 -那儿 -那个 -那会儿 -那里 -那么 -那么些 -那么样 -那时 -那些 -那样 -乃 -乃至 -呢 -能 -你 -你们 -您 -宁 -宁可 -宁肯 -宁愿 -哦 -呕 -啪达 -旁人 -呸 -凭 -凭借 -其 -其次 -其二 -其他 -其它 -其一 -其余 -其中 -起 -起见 -起见 -岂但 -恰恰相反 -前后 -前者 -且 -然而 -然后 -然则 -让 -人家 -任 -任何 -任凭 -如 -如此 -如果 -如何 -如其 -如若 -如上所述 -若 -若非 -若是 -啥 -上下 -尚且 -设若 -设使 -甚而 -甚么 -甚至 -省得 -时候 -什么 -什么样 -使得 -是 -是的 -首先 -谁 -谁知 -顺 -顺着 -似的 -虽 -虽然 -虽说 -虽则 -随 -随着 -所 -所以 -他 -他们 -他人 -它 -它们 -她 -她们 -倘 -倘或 -倘然 -倘若 -倘使 -腾 -替 -通过 -同 -同时 -哇 -万一 -往 -望 -为 -为何 -为了 -为什么 -为着 -喂 -嗡嗡 -我 -我们 -呜 -呜呼 -乌乎 -无论 -无宁 -毋宁 -嘻 -吓 -相对而言 -像 -向 -向着 -嘘 -呀 -焉 -沿 -沿着 -要 -要不 -要不然 -要不是 -要么 -要是 -也 -也罢 -也好 -一 -一般 -一旦 -一方面 -一来 -一切 -一样 -一则 -依 -依照 -矣 -以 -以便 -以及 -以免 -以至 -以至于 -以致 -抑或 -因 -因此 -因而 -因为 -哟 -用 -由 -由此可见 -由于 -有 -有的 -有关 -有些 -又 -于 -于是 -于是乎 -与 -与此同时 -与否 -与其 -越是 -云云 -哉 -再说 -再者 -在 -在下 -咱 -咱们 -则 -怎 -怎么 -怎么办 -怎么样 -怎样 -咋 -照 -照着 -者 -这 -这边 -这儿 -这个 -这会儿 -这就是说 -这里 -这么 -这么点儿 -这么些 -这么样 -这时 -这些 -这样 -正如 -吱 -之 -之类 -之所以 -之一 -只是 -只限 -只要 -只有 -至 -至于 -诸位 -着 -着呢 -自 -自从 -自个儿 -自各儿 -自己 -自家 -自身 -综上所述 -总的来看 -总的来说 -总的说来 -总而言之 -总之 -纵 -纵令 -纵然 -纵使 -遵照 -作为 -兮 -呃 -呗 -咚 -咦 -喏 -啐 -喔唷 -嗬 -嗯 -嗳 -打开天窗说亮话 -到目前为止 -赶早不赶晚 -常言说得好 -何乐而不为 -毫无保留地 -由此可见 -这就是说 -这么点儿 -综上所述 -总的来看 -总的来说 -总的说来 -总而言之 -相对而言 -除此之外 -反过来说 -恰恰相反 -如上所述 -换句话说 -具体地说 -具体说来 -另一方面 -与此同时 -一则通过 -毫无例外 -不然的话 -从此以后 -从古到今 -从古至今 -从今以后 -大张旗鼓 -从无到有 -从早到晚 -弹指之间 -不亦乐乎 -不知不觉 -不止一次 -不择手段 -不可开交 -不可抗拒 -不仅仅是 -不管怎样 -挨家挨户 -长此下去 -长话短说 -除此而外 -除此以外 -除此之外 -得天独厚 -川流不息 -长期以来 -挨门挨户 -挨门逐户 -多多少少 -多多益善 -二话不说 -更进一步 -二话没说 -分期分批 -风雨无阻 -归根到底 -归根结底 -反之亦然 -大面儿上 -倒不如说 -成年累月 -换句话说 -或多或少 -简而言之 -接连不断 -尽如人意 -尽心竭力 -尽心尽力 -尽管如此 -据我所知 -具体地说 -具体来说 -具体说来 -近几年来 -每时每刻 -屡次三番 -三番两次 -三番五次 -三天两头 -另一方面 -老老实实 -年复一年 -恰恰相反 -顷刻之间 -穷年累月 -千万千万 -日复一日 -如此等等 -如前所述 -如上所述 -一方面 -切不可 -顷刻间 -全身心 -另方面 -另一个 -猛然间 -默默地 -就是说 -近年来 -尽可能 -接下来 -简言之 -急匆匆 -即是说 -基本上 -换言之 -充其极 -充其量 -暗地里 -反之则 -比如说 -背地里 -背靠背 -并没有 -不得不 -不得了 -不得已 -不仅仅 -不经意 -不能不 -不外乎 -不由得 -不怎么 -不至于 -策略地 -差不多 -常言道 -常言说 -多年来 -多年前 -差一点 -敞开儿 -抽冷子 -大不了 -反倒是 -反过来 -大体上 -当口儿 -倒不如 -怪不得 -动不动 -看起来 -看上去 -看样子 -够瞧的 -到了儿 -呆呆地 -来不及 -来得及 -到头来 -连日来 -于是乎 -为什么 -这会儿 -换言之 -那会儿 -那么些 -那么样 -什么样 -反过来 -紧接着 -就是说 -要不然 -要不是 -一方面 -以至于 -自个儿 -自各儿 -之所以 -这么些 -这么样 -怎么办 -怎么样 -谁知 -顺着 -似的 -虽然 -虽说 -虽则 -随着 -所以 -他们 -他人 -它们 -她们 -倘或 -倘然 -倘若 -倘使 -要么 -要是 -也罢 -也好 -以便 -依照 -以及 -以免 -以至 -以致 -抑或 -因此 -因而 -因为 -由于 -有的 -有关 -有些 -于是 -与否 -与其 -越是 -云云 -一般 -一旦 -一来 -一切 -一样 -同时 -万一 -为何 -为了 -为着 -嗡嗡 -我们 -呜呼 -乌乎 -无论 -无宁 -沿着 -毋宁 -向着 -照着 -怎么 -咱们 -在下 -再说 -再者 -怎样 -这边 -这儿 -这个 -这里 -这么 -这时 -这些 -这样 -正如 -之类 -之一 -只是 -只限 -只要 -只有 -至于 -诸位 -着呢 -纵令 -纵然 -纵使 -遵照 -作为 -喔唷 -自从 -自己 -自家 -自身 -总之 -要不 -哎呀 -哎哟 -俺们 -按照 -吧哒 -罢了 -本着 -比方 -比如 -鄙人 -彼此 -别的 -别说 -并且 -不比 -不成 -不单 -不但 -不独 -不管 -不光 -不过 -不仅 -不拘 -不论 -不怕 -不然 -不如 -不特 -不惟 -不问 -不只 -朝着 -趁着 -除非 -除了 -此间 -此外 -从而 -但是 -当着 -的话 -等等 -叮咚 -对于 -多少 -而况 -而且 -而是 -而外 -而言 -而已 -尔后 -反之 -非但 -非徒 -否则 -嘎登 -各个 -各位 -各种 -各自 -根据 -故此 -固然 -关于 -果然 -果真 -哈哈 -何处 -何况 -何时 -哼唷 -呼哧 -还是 -还有 -或是 -或者 -极了 -及其 -及至 -即便 -即或 -即令 -即若 -即使 -既然 -既是 -继而 -加之 -假如 -假若 -假使 -鉴于 -几时 -较之 -接着 -结果 -进而 -尽管 -经过 -就是 -可见 -可是 -可以 -况且 -开始 -开外 -来着 -例如 -连同 -两者 -另外 -慢说 -漫说 -每当 -莫若 -某个 -某些 -哪边 -哪儿 -哪个 -哪里 -哪年 -哪怕 -哪天 -哪些 -哪样 -那边 -那儿 -那个 -那里 -那么 -那时 -那些 -那样 -乃至 -宁可 -宁肯 -宁愿 -你们 -啪达 -旁人 -凭借 -其次 -其二 -其他 -其它 -其一 -其余 -其中 -起见 -起见 -岂但 -前后 -前者 -然而 -然后 -然则 -人家 -任何 -任凭 -如此 -如果 -如何 -如其 -如若 -若非 -若是 -上下 -尚且 -设若 -设使 -甚而 -甚么 -甚至 -省得 -时候 -什么 -使得 -是的 -首先 -首先 -其次 -再次 -最后 -您们 -它们 -她们 -他们 -我们 -你是 -您是 -我是 -他是 -她是 -它是 -不是 -你们 -啊哈 -啊呀 -啊哟 -挨次 -挨个 -挨着 -哎呀 -哎哟 -俺们 -按理 -按期 -默然 -按时 -按说 -按照 -暗中 -暗自 -昂然 -八成 -倍感 -倍加 -本人 -本身 -本着 -并非 -别人 -必定 -比起 -比如 -比照 -鄙人 -毕竟 -必将 -必须 -并肩 -并没 -并排 -并且 -并无 -勃然 -不必 -不常 -不大 -不单 -不但 -而且 -不得 -不迭 -不定 -不独 -不对 -不妨 -不管 -不光 -不过 -不会 -不仅 -不拘 -不力 -不了 -不料 -不论 -不满 -不免 -不起 -不巧 -不然 -不日 -不少 -不胜 -不时 -不是 -不同 -不能 -不要 -不外 -不下 -不限 -不消 -不已 -不再 -不曾 -不止 -不只 -才能 -彻夜 -趁便 -趁机 -趁热 -趁势 -趁早 -趁着 -成心 -乘机 -乘势 -乘隙 -乘虚 -诚然 -迟早 -充分 -出来 -出去 -除此 -除非 -除开 -除了 -除去 -除却 -除外 -处处 -传说 -传闻 -纯粹 -此后 -此间 -此外 -此中 -次第 -匆匆 -从不 -从此 -从而 -从宽 -从来 -从轻 -从速 -从头 -从未 -从小 -从新 -从严 -从优 -从中 -从重 -凑巧 -存心 -达旦 -打从 -大大 -大抵 -大都 -大多 -大凡 -大概 -大家 -大举 -大略 -大约 -大致 -待到 -单纯 -单单 -但是 -但愿 -当场 -当儿 -当即 -当然 -当庭 -当头 -当下 -当真 -当中 -当着 -倒是 -到处 -到底 -到头 -得起 -的话 -的确 -等到 -等等 -顶多 -动辄 -陡然 -独自 -断然 -对于 -顿时 -多次 -多多 -多亏 -而后 -而论 -而且 -而是 -而外 -而言 -而已 -而又 -尔等 -反倒 -反而 -反手 -反之 -方才 -方能 -非常 -非但 -非得 -分头 -奋勇 -愤然 -更为 -更加 -根据 -个人 -各式 -刚才 -敢情 -该当 -嘎嘎 -否则 -赶快 -敢于 -刚好 -刚巧 -高低 -格外 -隔日 -隔夜 -公然 -过于 -果然 -果真 -光是 -关于 -共总 -姑且 -故此 -故而 -故意 -固然 -惯常 -毫不 -毫无 -很多 -何须 -好在 -何必 -何尝 -何妨 -何苦 -何况 -何止 -很少 -轰然 -后来 -呼啦 -哗啦 -互相 -忽地 -忽然 -话说 -或是 -伙同 -豁然 -恍然 -还是 -或许 -或者 -基本 -基于 -极大 -极度 -极端 -极力 -极其 -极为 -即便 -即将 -及其 -及至 -即刻 -即令 -即使 -几度 -几番 -几乎 -几经 -既然 -继而 -继之 -加上 -加以 -加之 -假如 -假若 -假使 -间或 -将才 -简直 -鉴于 -将近 -将要 -交口 -较比 -较为 -较之 -皆可 -截然 -截至 -藉以 -借此 -借以 -届时 -尽快 -近来 -进而 -进来 -进去 -尽管 -尽量 -尽然 -就算 -居然 -就此 -就地 -竟然 -究竟 -经常 -尽早 -精光 -经过 -就是 -局外 -举凡 -据称 -据此 -据实 -据说 -可好 -看来 -开外 -绝不 -决不 -据悉 -决非 -绝顶 -绝对 -绝非 -可见 -可能 -可是 -可以 -恐怕 -来讲 -来看 -快要 -况且 -拦腰 -牢牢 -老是 -累次 -累年 -理当 -理该 -理应 -例如 -立地 -立刻 -立马 -立时 -联袂 -连连 -连日 -路经 -临到 -连声 -连同 -连袂 -另外 -另行 -屡次 -屡屡 -缕缕 -率尔 -率然 -略加 -略微 -略为 -论说 -马上 -猛然 -没有 -每当 -每逢 -每每 -莫不 -莫非 -莫如 -莫若 -哪怕 -那么 -那末 -那些 -乃至 -难道 -难得 -难怪 -难说 -你们 -凝神 -宁可 -宁肯 -宁愿 -偶而 -偶尔 -碰巧 -譬如 -偏偏 -平素 -迫于 -扑通 -其次 -其后 -其实 -其它 -起初 -起来 -起首 -起头 -起先 -岂但 -岂非 -岂止 -恰逢 -恰好 -恰恰 -恰巧 -恰如 -恰似 -前后 -前者 -切莫 -切切 -切勿 -亲口 -亲身 -亲手 -亲眼 -亲自 -顷刻 -请勿 -取道 -权时 -全都 -全力 -全年 -全然 -然而 -然后 -人家 -人人 -仍旧 -仍然 -日见 -日渐 -日益 -日臻 -如常 -如次 -如果 -如今 -如期 -如若 -如上 -如下 -上来 -上去 -瑟瑟 -沙沙 -啊 -哎 -唉 -俺 -按 -吧 -把 -甭 -别 -嘿 -很 -乎 -会 -或 -既 -及 -啦 -了 -们 -你 -您 -哦 -砰 -啊 -你 -我 -他 -她 -它 -$ -0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -? -_ -“ -” -、 -。 -《 -》 -一 -一些 -一何 -一切 -一则 -一方面 -一旦 -一来 -一样 -一般 -一转眼 -万一 -上 -上下 -下 -不 -不仅 -不但 -不光 -不单 -不只 -不外乎 -不如 -不妨 -不尽 -不尽然 -不得 -不怕 -不惟 -不成 -不拘 -不料 -不是 -不比 -不然 -不特 -不独 -不管 -不至于 -不若 -不论 -不过 -不问 -与 -与其 -与其说 -与否 -与此同时 -且 -且不说 -且说 -两者 -个 -个别 -临 -为 -为了 -为什么 -为何 -为止 -为此 -为着 -乃 -乃至 -乃至于 -么 -之 -之一 -之所以 -之类 -乌乎 -乎 -乘 -也 -也好 -也罢 -了 -二来 -于 -于是 -于是乎 -云云 -云尔 -些 -亦 -人 -人们 -人家 -什么 -什么样 -今 -介于 -仍 -仍旧 -从 -从此 -从而 -他 -他人 -他们 -以 -以上 -以为 -以便 -以免 -以及 -以故 -以期 -以来 -以至 -以至于 -以致 -们 -任 -任何 -任凭 -似的 -但 -但凡 -但是 -何 -何以 -何况 -何处 -何时 -余外 -作为 -你 -你们 -使 -使得 -例如 -依 -依据 -依照 -便于 -俺 -俺们 -倘 -倘使 -倘或 -倘然 -倘若 -借 -假使 -假如 -假若 -傥然 -像 -儿 -先不先 -光是 -全体 -全部 -兮 -关于 -其 -其一 -其中 -其二 -其他 -其余 -其它 -其次 -具体地说 -具体说来 -兼之 -内 -再 -再其次 -再则 -再有 -再者 -再者说 -再说 -冒 -冲 -况且 -几 -几时 -凡 -凡是 -凭 -凭借 -出于 -出来 -分别 -则 -则甚 -别 -别人 -别处 -别是 -别的 -别管 -别说 -到 -前后 -前此 -前者 -加之 -加以 -即 -即令 -即使 -即便 -即如 -即或 -即若 -却 -去 -又 -又及 -及 -及其 -及至 -反之 -反而 -反过来 -反过来说 -受到 -另 -另一方面 -另外 -另悉 -只 -只当 -只怕 -只是 -只有 -只消 -只要 -只限 -叫 -叮咚 -可 -可以 -可是 -可见 -各 -各个 -各位 -各种 -各自 -同 -同时 -后 -后者 -向 -向使 -向着 -吓 -吗 -否则 -吧 -吧哒 -吱 -呀 -呃 -呕 -呗 -呜 -呜呼 -呢 -呵 -呵呵 -呸 -呼哧 -咋 -和 -咚 -咦 -咧 -咱 -咱们 -咳 -哇 -哈 -哈哈 -哉 -哎 -哎呀 -哎哟 -哗 -哟 -哦 -哩 -哪 -哪个 -哪些 -哪儿 -哪天 -哪年 -哪怕 -哪样 -哪边 -哪里 -哼 -哼唷 -唉 -唯有 -啊 -啐 -啥 -啦 -啪达 -啷当 -喂 -喏 -喔唷 -喽 -嗡 -嗡嗡 -嗬 -嗯 -嗳 -嘎 -嘎登 -嘘 -嘛 -嘻 -嘿 -嘿嘿 -因 -因为 -因了 -因此 -因着 -因而 -固然 -在 -在下 -在于 -地 -基于 -处在 -多 -多么 -多少 -大 -大家 -她 -她们 -好 -如 -如上 -如上所述 -如下 -如何 -如其 -如同 -如是 -如果 -如此 -如若 -始而 -孰料 -孰知 -宁 -宁可 -宁愿 -宁肯 -它 -它们 -对 -对于 -对待 -对方 -对比 -将 -小 -尔 -尔后 -尔尔 -尚且 -就 -就是 -就是了 -就是说 -就算 -就要 -尽 -尽管 -尽管如此 -岂但 -己 -已 -已矣 -巴 -巴巴 -并 -并且 -并非 -庶乎 -庶几 -开外 -开始 -归 -归齐 -当 -当地 -当然 -当着 -彼 -彼时 -彼此 -往 -待 -很 -得 -得了 -怎 -怎么 -怎么办 -怎么样 -怎奈 -怎样 -总之 -总的来看 -总的来说 -总的说来 -总而言之 -恰恰相反 -您 -惟其 -慢说 -我 -我们 -或 -或则 -或是 -或曰 -或者 -截至 -所 -所以 -所在 -所幸 -所有 -才 -才能 -打 -打从 -把 -抑或 -拿 -按 -按照 -换句话说 -换言之 -据 -据此 -接着 -故 -故此 -故而 -旁人 -无 -无宁 -无论 -既 -既往 -既是 -既然 -时候 -是 -是以 -是的 -曾 -替 -替代 -最 -有 -有些 -有关 -有及 -有时 -有的 -望 -朝 -朝着 -本 -本人 -本地 -本着 -本身 -来 -来着 -来自 -来说 -极了 -果然 -果真 -某 -某个 -某些 -某某 -根据 -欤 -正值 -正如 -正巧 -正是 -此 -此地 -此处 -此外 -此时 -此次 -此间 -毋宁 -每 -每当 -比 -比及 -比如 -比方 -没奈何 -沿 -沿着 -漫说 -焉 -然则 -然后 -然而 -照 -照着 -犹且 -犹自 -甚且 -甚么 -甚或 -甚而 -甚至 -甚至于 -用 -用来 -由 -由于 -由是 -由此 -由此可见 -的 -的确 -的话 -直到 -相对而言 -省得 -看 -眨眼 -着 -着呢 -矣 -矣乎 -矣哉 -离 -竟而 -第 -等 -等到 -等等 -简言之 -管 -类如 -紧接着 -纵 -纵令 -纵使 -纵然 -经 -经过 -结果 -给 -继之 -继后 -继而 -综上所述 -罢了 -者 -而 -而且 -而况 -而后 -而外 -而已 -而是 -而言 -能 -能否 -腾 -自 -自个儿 -自从 -自各儿 -自后 -自家 -自己 -自打 -自身 -至 -至于 -至今 -至若 -致 -般的 -若 -若夫 -若是 -若果 -若非 -莫不然 -莫如 -莫若 -虽 -虽则 -虽然 -虽说 -被 -要 -要不 -要不是 -要不然 -要么 -要是 -譬喻 -譬如 -让 -许多 -论 -设使 -设或 -设若 -诚如 -诚然 -该 -说来 -诸 -诸位 -诸如 -谁 -谁人 -谁料 -谁知 -贼死 -赖以 -赶 -起 -起见 -趁 -趁着 -越是 -距 -跟 -较 -较之 -边 -过 -还 -还是 -还有 -还要 -这 -这一来 -这个 -这么 -这么些 -这么样 -这么点儿 -这些 -这会儿 -这儿 -这就是说 -这时 -这样 -这次 -这般 -这边 -这里 -进而 -连 -连同 -逐步 -通过 -遵循 -遵照 -那 -那个 -那么 -那么些 -那么样 -那些 -那会儿 -那儿 -那时 -那样 -那般 -那边 -那里 -都 -鄙人 -鉴于 -针对 -阿 -除 -除了 -除外 -除开 -除此之外 -除非 -随 -随后 -随时 -随着 -难道说 -非但 -非徒 -非特 -非独 -靠 -顺 -顺着 -首先 -！ -， -： -； -？ \ No newline at end of file diff --git a/winnow/text_search/textlib.py b/winnow/text_search/textlib.py deleted file mode 100644 index 8a991aac..00000000 --- a/winnow/text_search/textlib.py +++ /dev/null @@ -1,112 +0,0 @@ -# -*- coding: utf-8 -*- -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - -import logging -import os -import re -import sys - -logger = logging.getLogger(__file__) -logging.basicConfig( - format="[%(asctime)s - %(filename)s:line %(lineno)s] %(message)s", datefmt="%d %b %H:%M:%S", level=logging.INFO -) - -en_stop_fname = os.path.join(os.path.dirname(__file__), "stopwords_en.txt") -zh_stop_fname = os.path.join(os.path.dirname(__file__), "stopwords_zh.txt") -ENGLISH_STOP_WORDS = set(map(str.strip, open(en_stop_fname).readlines())) -CHINESE_STOP_WORDS = set(map(str.strip, open(zh_stop_fname).readlines())) - -if 3 == sys.version_info[0]: - CHN_DEL_SET = "， 。 、 ！ 《 》 “ ” ； ？ ‘ ’ ".split() -else: - CHN_DEL_SET = [x.decode("utf-8") for x in "， 。 、 ！ 《 》 “ ” ； ？ ‘ ’ ".split()] - - -class TextTool: - @staticmethod - def tokenize(input_str, clean=True, language="en", remove_stopword=False): - if "en" == language: # English - # delete non-ascii chars - # sent = input_str.decode('utf-8').encode('ascii', 'ignore') - sent = input_str - if clean: - sent = sent.replace("\r", " ") - sent = re.sub(r"[^A-Za-z0-9]", " ", sent).strip().lower() - tokens = sent.split() - if remove_stopword: - tokens = [x for x in tokens if x not in ENGLISH_STOP_WORDS] - else: # Chinese - # sent = input_str #string.decode('utf-8') - sent = input_str.decode("utf-8") - - if clean: - for elem in CHN_DEL_SET: - sent = sent.replace(elem, "") - sent = sent.encode("utf-8") - sent = re.sub("[A-Za-z]", "", sent) - tokens = [x for x in sent.split()] - if remove_stopword: - tokens = [x for x in tokens if x not in CHINESE_STOP_WORDS] - - return tokens - - -class Vocabulary(object): - """Simple vocabulary wrapper.""" - - def __init__(self, encoding): - self.word2idx = {} - self.idx2word = {} - self.encoding = encoding - - def add(self, word): - if word not in self.word2idx: - idx = len(self.word2idx) - self.word2idx[word] = idx - self.idx2word[idx] = word - - def find(self, word): - return self.word2idx.get(word, -1) - - def __getitem__(self, index): - return self.idx2word[index] - - def __call__(self, word): - if word not in self.word2idx: - if "gru" in self.encoding: - return self.word2idx[""] - else: - raise Exception("word out of vocab: %s" % word) - else: - return self.word2idx[word] - - def __len__(self): - return len(self.word2idx) - - -if __name__ == "__main__": - test_strs = """a Dog is running -The dog runs -dogs-x runs""".split( - "\n" - ) - - for t in test_strs: - print(t, "->", TextTool.tokenize(t, clean=True, language="en"), "->", TextTool.tokenize(t, "en", True)) - - test_strs = """一间 干净 整洁 的 房间 。 -一只 黄色 的 小狗 趴在 长椅 上""".split( - "\n" - ) - - for t in test_strs: - print( - t, - "->", - " ".join(TextTool.tokenize(t, clean=True, language="zh")), - "->", - " ".join(TextTool.tokenize(t, "zh", True)), - ) diff --git a/winnow/text_search/trainer.py b/winnow/text_search/trainer.py deleted file mode 100644 index 9ab3423d..00000000 --- a/winnow/text_search/trainer.py +++ /dev/null @@ -1,275 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - -from __future__ import print_function - -import argparse -import importlib -import json -import os -import shutil -import sys -import time - -import numpy as np -import torch -from torch.utils.tensorboard import SummaryWriter - -import winnow.text_search.data_provider as data -import winnow.text_search.evaluation as evaluation -import winnow.text_search.util as util -from .bigfile import BigFile -from .common import ROOT_PATH -from .generic_utils import Progbar -from .model import get_model, get_we -from .txt2vec import get_txt2vec - - -def parse_args(): - parser = argparse.ArgumentParser("W2VVPP training script.") - parser.add_argument("--rootpath", type=str, default=ROOT_PATH, help="path to datasets. (default: %s)" % ROOT_PATH) - parser.add_argument("trainCollection", type=str, help="train collection") - parser.add_argument("valCollection", type=str, help="validation collection") - parser.add_argument( - "--overwrite", type=int, default=0, choices=[0, 1], help="overwrite existed vocabulary file. (default: 0)" - ) - parser.add_argument( - "--val_set", type=str, default="", help="validation collection set (setA, setB). (default: setA)" - ) - parser.add_argument( - "--metric", - type=str, - default="mir", - choices=["r1", "r5", "medr", "meanr", "mir"], - help="performance metric on validation set", - ) - parser.add_argument("--num_epochs", default=80, type=int, help="Number of training epochs.") - parser.add_argument("--batch_size", default=128, type=int, help="Size of a training mini-batch.") - parser.add_argument("--workers", default=2, type=int, help="Number of data loader workers.") - parser.add_argument( - "--model_prefix", default="runs_0", type=str, help="Path to save the model and Tensorboard log." - ) - parser.add_argument( - "--config_name", - type=str, - default="mean_pyresnext-101_rbps13k", - help="model configuration file. (default: mean_pyresnext-101_rbps13k", - ) - - args = parser.parse_args() - return args - - -def load_config(config_path): - module = importlib.import_module(config_path) - return module.config() - - -def main(): - opt = parse_args() - print(json.dumps(vars(opt), indent=2)) - - rootpath = opt.rootpath - train_collection = opt.trainCollection - val_collection = opt.valCollection - val_set = opt.val_set - - config = load_config("winnow.text_search.configs.%s" % opt.config_name) - - model_path = os.path.join( - rootpath, train_collection, "w2vvpp_train", val_collection, val_set, opt.config_name, opt.model_prefix - ) - if util.checkToSkip(os.path.join(model_path, "model_best.pth.tar"), opt.overwrite): - sys.exit(0) - util.makedirs(model_path) - - global writer - writer = SummaryWriter(log_dir=model_path, flush_secs=5) - - collections = {"train": train_collection, "val": val_collection} - - capfiles = {"train": "%s.caption.txt", "val": os.path.join(val_set, "%s.caption.txt")} - cap_file_paths = { - x: os.path.join(rootpath, collections[x], "TextData", capfiles[x] % collections[x]) for x in collections - } - hijack = 500 - vis_feat_files = { - x: BigFile(os.path.join(rootpath, collections[x], "FeatureData", config.vid_feat), hijack=hijack) - for x in collections - } - config.vis_fc_layers = map(int, config.vis_fc_layers.split("-")) - # - config.vis_fc_layers[0] = hijack or vis_feat_files["train"].ndims - - bow_encoding, w2v_encoding, rnn_encoding = config.text_encoding.split("@") - rnn_encoding, config.pooling = rnn_encoding.split("_", 1) - - bow_vocab_file = os.path.join( - rootpath, train_collection, "TextData", "vocab", "%s_%d.pkl" % (bow_encoding, config.threshold) - ) - config.t2v_bow = get_txt2vec(bow_encoding)(bow_vocab_file, norm=config.bow_norm) - - w2v_data_path = os.path.join(rootpath, "word2vec", "flickr", "vec500flickr30m") - config.t2v_w2v = get_txt2vec(w2v_encoding)(w2v_data_path) - - rnn_vocab_file = os.path.join( - rootpath, train_collection, "TextData", "vocab", "%s_%d.pkl" % (rnn_encoding, config.threshold) - ) - config.t2v_idx = get_txt2vec("idxvec")(rnn_vocab_file) - if config.we_dim == 500: - config.we = get_we(config.t2v_idx.vocab, w2v_data_path) - - config.txt_fc_layers = map(int, config.txt_fc_layers.split("-")) - if config.pooling == "mean_last": - config.txt_fc_layers[0] = config.rnn_size * 2 + config.t2v_w2v.ndims + config.t2v_bow.ndims - else: - config.txt_fc_layers[0] = config.rnn_size + config.t2v_w2v.ndims + config.t2v_bow.ndims - - # Construct the model - model = get_model("w2vvpp")(config) - print(model.vis_net) - print(model.txt_net) - - data_loaders = { - x: data.pair_provider( - { - "vis_feat": vis_feat_files[x], - "capfile": cap_file_paths[x], - "pin_memory": True, - "batch_size": opt.batch_size, - "num_workers": opt.workers, - "shuffle": (x == "train"), - } - ) - for x in collections - } - - # Train the Model - best_perf = 0 - no_impr_counter = 0 - val_perf_hist_fout = open(os.path.join(model_path, "val_perf_hist.txt"), "w") - for epoch in range(opt.num_epochs): - - print("Epoch[{0} / {1}] LR: {2}".format(epoch, opt.num_epochs, model.learning_rate)) - print("-" * 10) - writer.add_scalar("train/learning_rate", model.learning_rate[0], epoch) - # train for one epoch - train(model, data_loaders["train"], epoch) - - # evaluate on validation set - cur_perf = validate(model, data_loaders["val"], epoch, measure=config.measure, metric=opt.metric) - model.lr_step(val_value=cur_perf) - - print(" * Current perf: {}\n * Best perf: {}\n".format(cur_perf, best_perf)) - val_perf_hist_fout.write("epoch_%d:\nText2Video(%s): %f\n" % (epoch, opt.metric, cur_perf)) - val_perf_hist_fout.flush() - - # remember best performance and save checkpoint - is_best = cur_perf > best_perf - best_perf = max(cur_perf, best_perf) - save_checkpoint( - {"epoch": epoch + 1, "model": model.state_dict(), "best_perf": best_perf, "config": config, "opt": opt}, - is_best, - logdir=model_path, - only_best=True, - filename="checkpoint_epoch_%s.pth.tar" % epoch, - ) - if is_best: - no_impr_counter = 0 - else: - no_impr_counter += 1 - if no_impr_counter > 10: - print("Early stopping happended.\n") - break - - val_perf_hist_fout.close() - message = "best performance on validation:\n Text to video({}): {}".format(opt.metric, best_perf) - print(message) - with open(os.path.join(model_path, "val_perf.txt"), "w") as fout: - fout.write(message) - - -def train(model, train_loader, epoch): - # average meters to record the training statistics - batch_time = util.AverageMeter() - data_time = util.AverageMeter() - - # switch to train mode - model.switch_to_train() - - progbar = Progbar(len(train_loader.dataset)) - end = time.time() - for i, train_data in enumerate(train_loader): - - data_time.update(time.time() - end) - - vis_input, txt_input, _, _, _ = train_data - loss = model.train(vis_input, txt_input) - - progbar.add( - vis_input.size(0), values=[("data_time", data_time.val), ("batch_time", batch_time.val), ("loss", loss)] - ) - - # measure elapsed time - batch_time.update(time.time() - end) - end = time.time() - - # Record logs in tensorboard - writer.add_scalar("train/Loss", loss, model.iters) - - -def validate(model, val_loader, epoch, measure="cosine", metric="mir"): - # compute the encoding for all the validation videos and captions - vis_embs, txt_embs, vis_ids, txt_ids = evaluation.encode_data(model, val_loader) - - keep_vis_order = [] - keep_vis_ids = [] - for i, vid in enumerate(vis_ids): - if vid not in keep_vis_ids: - keep_vis_order.append(i) - keep_vis_ids.append(vid) - vis_embs = vis_embs[keep_vis_order] - vis_ids = keep_vis_ids - - # video retrieval - txt2vis_sim = evaluation.compute_sim(txt_embs, vis_embs, measure) - # (r1, r5, r10, medr, meanr, mir) = evaluation.eval_qry2retro(txt2vis_sim, n_qry=1) - inds = np.argsort(txt2vis_sim, axis=1) - label_matrix = np.zeros(inds.shape) - for index in range(inds.shape[0]): - ind = inds[index][::-1] - label_matrix[index][np.where(np.array(vis_ids)[ind] == txt_ids[index].split("#")[0])[0]] = 1 - - (r1, r5, r10, medr, meanr, mir, mAP) = evaluation.eval(label_matrix) - sum_recall = r1 + r5 + r10 - print(" * Text to video:") - print(" * r_1_5_10: {}".format([round(r1, 3), round(r5, 3), round(r10, 3)])) - print(" * medr, meanr, mir: {}".format([round(medr, 3), round(meanr, 3), round(mir, 3)])) - print(" * mAP: {}".format(round(mAP, 3))) - print(" * " + "-" * 10) - - writer.add_scalar("val/r1", r1, epoch) - writer.add_scalar("val/r5", r5, epoch) - writer.add_scalar("val/r10", r10, epoch) - writer.add_scalar("val/medr", medr, epoch) - writer.add_scalar("val/meanr", meanr, epoch) - writer.add_scalar("val/mir", mir, epoch) - writer.add_scalar("val/mAP", mAP, epoch) - - return locals().get(metric, mir) - - -def save_checkpoint(state, is_best, filename="checkpoint.pth.tar", only_best=False, logdir=""): - resfile = os.path.join(logdir, filename) - torch.save(state, resfile) - if is_best: - shutil.copyfile(resfile, os.path.join(logdir, "model_best.pth.tar")) - - if only_best: - os.remove(resfile) - - -if __name__ == "__main__": - main() diff --git a/winnow/text_search/txt2vec.py b/winnow/text_search/txt2vec.py deleted file mode 100644 index 1a8c7475..00000000 --- a/winnow/text_search/txt2vec.py +++ /dev/null @@ -1,139 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - - -import pickle - -import numpy as np - -from winnow.text_search.bigfile import BigFile -from winnow.text_search.common import logger -from winnow.text_search.textlib import TextTool - - -def get_lang(data_path): - return "en" - - -class Txt2Vec(object): - """ - norm: 0 no norm, 1 l_1 norm, 2 l_2 norm - """ - - def __init__(self, data_path, norm=0, clean=True): - logger.info(self.__class__.__name__ + " initializing ...") - self.data_path = data_path - self.norm = norm - self.lang = get_lang(data_path) - self.clean = clean - assert norm in [0, 1, 2], "invalid norm %s" % norm - - def _preprocess(self, query): - words = TextTool.tokenize(query, clean=self.clean, language=self.lang) - return words - - def _do_norm(self, vec): - assert 1 == self.norm or 2 == self.norm - norm = np.linalg.norm(vec, self.norm) - return vec / (norm + 1e-10) # avoid divide by ZERO - - def _encoding(self, words): - raise Exception("encoding not implemented yet!") - - def encoding(self, query): - words = self._preprocess(query) - vec = self._encoding(words) - if self.norm > 0: - return self.do_norm(vec) - return vec - - -class BowVec(Txt2Vec): - def __init__(self, data_path, norm=0, clean=True): - super(BowVec, self).__init__(data_path, norm, clean) - self.vocab = pickle.load(open(data_path, "rb")) - self.ndims = len(self.vocab) - logger.info("vob size: %d, vec dim: %d" % (len(self.vocab), self.ndims)) - - def _encoding(self, words): - vec = np.zeros( - self.ndims, - ) - - for word in words: - idx = self.vocab.find(word) - if idx >= 0: - vec[idx] += 1 - return vec - - -class W2Vec(Txt2Vec): - def __init__(self, data_path, norm=0, clean=True): - super(W2Vec, self).__init__(data_path, norm, clean) - self.w2v = BigFile(data_path) - vocab_size, self.ndims = self.w2v.shape() - logger.info("vob size: %d, vec dim: %d" % (vocab_size, self.ndims)) - - def _encoding(self, words): - renamed, vectors = self.w2v.read(words) - - if len(vectors) > 0: - vec = np.array(vectors).mean(axis=0) - else: - vec = np.zeros( - self.ndims, - ) - return vec - - -class IndexVec(Txt2Vec): - def __init__(self, data_path, clean=True): - super(IndexVec, self).__init__(data_path, 0, clean) - self.vocab = pickle.load(open(data_path, "rb")) - logger.info("vob size: %s" % (len(self.vocab))) - - def _preprocess(self, query): - words = TextTool.tokenize(query, clean=self.clean, language=self.lang, remove_stopword=False) - words = [""] + words + [""] - return words - - def _encoding(self, words): - return np.array([self.vocab(word) for word in words]) - - -class BowVecNSW(BowVec): - def __init__(self, data_path, norm=0, clean=True): - super(BowVecNSW, self).__init__(data_path, norm, clean) - if "_nsw" not in data_path: - logger.error("WARNING: loaded a vocabulary that contains stopwords") - - def _preprocess(self, query): - words = TextTool.tokenize(query, clean=self.clean, language=self.lang, remove_stopword=True) - return words - - -class W2VecNSW(W2Vec): - def _preprocess(self, query): - words = TextTool.tokenize(query, clean=self.clean, language=self.lang, remove_stopword=True) - return words - - -NAME_TO_T2V = {"bow": BowVec, "bow_nsw": BowVecNSW, "w2v": W2Vec, "w2v_nsw": W2VecNSW, "idxvec": IndexVec} - - -def get_txt2vec(name): - assert name in NAME_TO_T2V - return NAME_TO_T2V[name] - - -if __name__ == "__main__": - t2v = BowVec("VisualSearch/tgif-msrvtt10k/TextData/vocab/bow_5.pkl") - t2v = BowVecNSW("VisualSearch/tgif-msrvtt10k/TextData/vocab/bow_nsw_5.pkl") - t2v = BowVecNSW("VisualSearch/tgif-msrvtt10k/TextData/vocab/bow_5.pkl") - t2v = W2Vec("VisualSearch/word2vec/flickr/vec500flickr30m") - t2v = W2VecNSW("VisualSearch/word2vec/flickr/vec500flickr30m") - - vec = t2v.encoding("a dog runs on grass") - print(vec.shape) diff --git a/winnow/text_search/util.py b/winnow/text_search/util.py deleted file mode 100644 index 461f7882..00000000 --- a/winnow/text_search/util.py +++ /dev/null @@ -1,105 +0,0 @@ -# -------------------------------------------------------- -# Pytorch W2VV++ -# Written by Xirong Li & Chaoxi Xu -# -------------------------------------------------------- - -import logging -import os -import time -from collections import OrderedDict -from functools import wraps - -logger = logging.getLogger(__file__) -logging.basicConfig( - format="[%(asctime)s - %(filename)s:line %(lineno)s] %(message)s", datefmt="%d %b %H:%M:%S", level=logging.INFO -) - - -def checkToSkip(filename, overwrite): - if os.path.exists(filename): - if overwrite: - logging.info("%s exists. overwrite", filename) - return 0 - else: - logging.info("%s exists. quit", filename) - return 1 - return 0 - - -def makedirs(path): - if not os.path.exists(path): - os.makedirs(path) - - -def makedirsforfile(filename): - makedirs(os.path.dirname(filename)) - - -def timer(fn): - @wraps(fn) - def compute_time(*args, **kwargs): - start_time = time.time() - - ret = fn(*args, **kwargs) - - elapsed_time = time.time() - start_time - print(fn.__name__ + " execution time: %.3f seconds\n" % elapsed_time) - - return ret - - return compute_time - - -class AverageMeter(object): - """Computes and stores the average and current value""" - - def __init__(self): - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / (0.0001 + self.count) - - def __str__(self): - """String representation for logging""" - # for values that should be recorded exactly e.g. iteration number - if self.count == 0: - return str(self.val) - # for stats - return "%.4f (%.4f)" % (self.val, self.avg) - - -class LogCollector(object): - """A collection of logging objects that can change from train to val""" - - def __init__(self): - # to keep the order of logged variables deterministic - self.meters = OrderedDict() - - def update(self, k, v, n=1): - # create a new meter if previously not recorded - if k not in self.meters: - self.meters[k] = AverageMeter() - self.meters[k].update(v, n) - - def __str__(self): - """Concatenate the meters in one log line""" - s = "" - for i, (k, v) in enumerate(self.meters.iteritems()): - if i > 0: - s += " " - s += k + " " + str(v) - return s - - def tb_log(self, tb_logger, prefix="", step=None): - """Log using tensorboard""" - for k, v in self.meters.iteritems(): - tb_logger.add_scalar(prefix + k, v.val, step=step) diff --git a/winnow/utils/download.py b/winnow/utils/download.py index 128f238e..7cf51342 100644 --- a/winnow/utils/download.py +++ b/winnow/utils/download.py @@ -5,9 +5,9 @@ from pathlib import Path from typing import Iterable, List -import yt_dlp +import youtube_dl from tqdm import tqdm -from yt_dlp.utils import YoutubeDLError +from youtube_dl.utils import YoutubeDLError from winnow.pipeline.progress_monitor import ProgressMonitor, BaseProgressMonitor @@ -129,7 +129,7 @@ def download_video( "outtmpl": output_template, } - with yt_dlp.YoutubeDL(ydl_opts) as ydl: + with youtube_dl.YoutubeDL(ydl_opts) as ydl: # Determine destination file name video_info = ydl.extract_info(video_url, download=False) file_name = ydl.prepare_filename(video_info) diff --git a/winnow/utils/logging.py b/winnow/utils/logging.py index ec470f28..f1c70277 100644 --- a/winnow/utils/logging.py +++ b/winnow/utils/logging.py @@ -1,17 +1,14 @@ """A number of functions to apply logging configuration presets.""" -import inspect import logging import sys -def configure_logging_cli(error_log_file="processing_error.log") -> logging.Logger: +def configure_logging_cli(error_log_file="processing_error.log"): """Configure logging for CLI scripts.""" root_logger = logging.getLogger() winnow_logger = logging.getLogger("winnow") root_logger.setLevel(logging.ERROR) - root_handler = logging.StreamHandler(sys.stdout) - root_handler.setFormatter(logging.Formatter("[%(asctime)s: %(levelname)s] [%(name)s] %(message)s")) - root_logger.addHandler(root_handler) + root_logger.addHandler(logging.StreamHandler(sys.stdout)) winnow_logger.setLevel(logging.INFO) # Write errors to the log file @@ -20,41 +17,3 @@ def configure_logging_cli(error_log_file="processing_error.log") -> logging.Logg error_log_handler.setLevel(logging.ERROR) winnow_logger.addHandler(error_log_handler) return winnow_logger - - -def configure_logging_server( - name: str, - level: int = logging.INFO, - error_log_file: str = "processing_error.log", - format: str = "[%(asctime)s: %(levelname)s] [%(name)s] %(message)s", -) -> logging.Logger: - """Configure logging for CLI scripts.""" - # configure winnow logger - root_logger = logging.getLogger() - winnow_logger = logging.getLogger("winnow") - root_logger.setLevel(logging.ERROR) - root_logger.addHandler(logging.StreamHandler(sys.stdout)) - winnow_logger.setLevel(level) - - # Write errors to the log file - error_log_handler = logging.FileHandler(error_log_file) - error_log_handler.setFormatter(logging.Formatter(format)) - error_log_handler.setLevel(logging.ERROR) - winnow_logger.addHandler(error_log_handler) - - # Configure requested logger - logger = logging.getLogger(name) - logger.setLevel(level) - return logger - - -def logger_name(owner) -> str: - """Get appropriate logger name for an owner.""" - if inspect.isclass(owner) or inspect.isfunction(owner) or inspect.ismethod(owner): - return f"{inspect.getmodule(owner).__name__}.{owner.__qualname__}" - return logger_name(type(owner)) - - -def get_logger(owner) -> logging.Logger: - """Get logger for the given owner object.""" - return logging.getLogger(logger_name(owner))