-
Notifications
You must be signed in to change notification settings - Fork 114
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Signed-off-by: Patrick Weizhi Xu <[email protected]>
- Loading branch information
Showing
4 changed files
with
357 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,244 @@ | ||
| import os | ||
| import time | ||
| import numpy as np | ||
| import psutil | ||
| from benchmark.algorithms.base import BaseANN | ||
| from benchmark.datasets import DATASETS, download_accelerated | ||
|
|
||
| import bbannpy | ||
|
|
||
|
|
||
| class BbANN(BaseANN): | ||
| def __init__(self, metric, index_params): | ||
| self.name = "BBANN" | ||
| self.metric = metric | ||
| self.index_params = index_params | ||
| self.identifier = index_params.get("identifier") | ||
| self.para = bbannpy.BBAnnParameters() | ||
| print("init BbAnn with the following parameters") | ||
| for key in index_params: | ||
| print(key, index_params[key]) | ||
| if hasattr(self.para, key): | ||
| setattr(self.para, key, index_params[key]) | ||
|
|
||
| def set_query_arguments(self, query_args): | ||
| print("query BbAnn with the following parameters") | ||
| for key in query_args: | ||
| print(key, query_args[key]) | ||
| if hasattr(self.para, key): | ||
| setattr(self.para, key, query_args[key]) | ||
| pass | ||
|
|
||
| def done(self): | ||
| pass | ||
|
|
||
| def track(self): | ||
| return "T2" | ||
|
|
||
| def index_name(self): | ||
| """ | ||
| File name for the index. | ||
| """ | ||
| return f"{self.identifier}_index" | ||
|
|
||
| def create_index_dir(self, dataset): | ||
| """ | ||
| Return a folder name, in which we would store the index. | ||
| """ | ||
| print("trying to create index dir:", self.index_params) | ||
| if "overrideIndexPath" in self.index_params: | ||
| print("Override index path:", self.index_params["overrideIndexPath"]) | ||
| return self.index_params["overrideIndexPath"] | ||
| index_dir = os.path.join(os.getcwd(), "data", "indices") | ||
| os.makedirs(index_dir, mode=0o777, exist_ok=True) | ||
| index_dir = os.path.join(index_dir, "T2") | ||
| os.makedirs(index_dir, mode=0o777, exist_ok=True) | ||
| index_dir = os.path.join(index_dir, self.__str__()) | ||
| os.makedirs(index_dir, mode=0o777, exist_ok=True) | ||
| index_dir = os.path.join(index_dir, dataset.short_name()) | ||
| os.makedirs(index_dir, mode=0o777, exist_ok=True) | ||
| index_dir = os.path.join(index_dir, self.index_name()) | ||
| os.makedirs(index_dir, mode=0o777, exist_ok=True) | ||
| print(index_dir) | ||
| return index_dir | ||
|
|
||
| def get_index_dir(self, dataset): | ||
| if "overrideIndexPath" in self.index_params: | ||
| print("Override index path:", self.index_params["overrideIndexPath"]) | ||
| return self.index_params["overrideIndexPath"] | ||
| index_dir = os.path.join(os.getcwd(), "data", "indices") | ||
| index_dir = os.path.join(index_dir, "T2") | ||
| index_dir = os.path.join(index_dir, self.__str__()) | ||
| index_dir = os.path.join(index_dir, dataset.short_name()) | ||
| index_dir = os.path.join(index_dir, self.index_name()) | ||
| return index_dir | ||
|
|
||
| def get_index_components(self, dataset): | ||
| index_components = [ | ||
| 'bucket-centroids.bin', 'hnsw-index.bin', 'cluster-combine_ids.bin' | ||
| ] | ||
|
|
||
| K1 = self.index_params.get("K1") | ||
| for i in range(K1): | ||
| raw_data = 'cluster-' + str(i) + '-global_ids.bin' | ||
| global_ids = 'cluster-' + str(i) + '-raw_data.bin' | ||
| index_components = index_components + [raw_data, global_ids] | ||
|
|
||
| ds = DATASETS[dataset]() | ||
| if 'vector_use_sq' in self.index_params and self.index_params['vector_use_sq']: | ||
| index_components = index_components + ['meta'] | ||
|
|
||
| if 'use_hnsw_sq' in self.index_params and self.index_params['use_hnsw_sq']: | ||
| index_components = index_components + ['hnsw-index.bin.codes'] | ||
|
|
||
| return index_components | ||
|
|
||
| def index_files_to_store(self, dataset): | ||
| """ | ||
| Specify a triplet with the local directory path of index files, | ||
| the common prefix name of index component(s) and a list of | ||
| index components that need to be uploaded to (after build) | ||
| or downloaded from (for search) cloud storage. | ||
| For local directory path under docker environment, please use | ||
| a directory under | ||
| data/indices/track(T1 or T2)/algo.__str__()/DATASETS[dataset]().short_name() | ||
| """ | ||
|
|
||
| return [self.create_index_dir(DATASETS[dataset]()) # local_dir | ||
| , "" # index_prefix | ||
| , self.get_index_components(dataset) # components | ||
| ] | ||
|
|
||
| def set_index_type(self, ds_distance, ds_dtype): | ||
| if ds_distance == "euclidean": | ||
| self.para.metric = bbannpy.L2 | ||
| elif ds_distance == "ip": | ||
| self.para.metric = bbannpy.INNER_PRODUCT | ||
| else: | ||
| print("Unsuported distance function.") | ||
| return False | ||
|
|
||
| if not hasattr(self, 'index'): | ||
| if ds_dtype == "float32": | ||
| self.index = bbannpy.FloatIndex(self.para.metric) | ||
| elif ds_dtype == "int8": | ||
| self.index = bbannpy.Int8Index(self.para.metric) | ||
| elif ds_dtype == "uint8": | ||
| self.index = bbannpy.UInt8Index(self.para.metric) | ||
| else: | ||
| print("Unsupported data type.") | ||
| return False | ||
| return True | ||
|
|
||
| def fit(self, dataset): | ||
| """ | ||
| Build the index for the data points given in dataset name. | ||
| Assumes that after fitting index is loaded in memory. | ||
| """ | ||
| ds = DATASETS[dataset]() | ||
| d = ds.d | ||
| index_dir = self.create_index_dir(ds) | ||
| if not self.set_index_type(ds.distance(), ds.dtype): | ||
| return False | ||
|
|
||
| self.para.dataFilePath = ds.get_dataset_fn() | ||
| self.para.indexPrefixPath = index_dir+"/" | ||
|
|
||
| start = time.time() | ||
| self.index.build(self.para) | ||
| end = time.time() | ||
| print("bbAnn index built in %.3f s" % (end - start)) | ||
| print(f"Loading index from {self.para.indexPrefixPath}") | ||
| self.index.load_index(self.para.indexPrefixPath, self.para) | ||
|
|
||
| def load_index(self, dataset): | ||
| """ | ||
| Load the index for dataset. Returns False if index | ||
| is not available, True otherwise. | ||
| Checking the index usually involves the dataset name | ||
| and the index build paramters passed during construction. | ||
| """ | ||
| ds = DATASETS[dataset]() | ||
| index_dir = self.get_index_dir(ds) | ||
| if not (os.path.exists(index_dir)) and 'url' not in self.index_params: | ||
| return False | ||
| if not self.set_index_type(ds.distance(), ds.dtype): | ||
| return False | ||
| self.para.indexPrefixPath = index_dir + "/" | ||
|
|
||
| index_components = self.get_index_components(dataset) | ||
| for component in index_components: | ||
| index_file = self.para.indexPrefixPath + component | ||
| if not (os.path.exists(index_file)): | ||
| if 'url' in self.index_params: | ||
| index_file_source = self.index_params['url'] + '/' + component | ||
| print(f"Downloading index in background. This can take a while.") | ||
| download_accelerated(index_file_source, index_file, quiet=True) | ||
| else: | ||
| return False | ||
|
|
||
| print(f"Loading index from {self.para.indexPrefixPath}") | ||
| self.index.load_index(self.para.indexPrefixPath, self.para) | ||
| return True | ||
|
|
||
| def query(self, X, k): | ||
| """Carry out a batch query for k-NN of query set X.""" | ||
| nq, dim = (np.shape(X)) | ||
| self.res, self.query_dists = self.index.batch_search( | ||
| X, dim, nq, k, self.para) | ||
| # print(self.res) | ||
|
|
||
| def range_query(self, X, radius): | ||
| """ | ||
| Carry out a batch query for range search with | ||
| radius. | ||
| """ | ||
| nq, dim = (np.shape(X)) | ||
| # print(f"Query index with para {self.para}") | ||
| self.rangeres_lim, (self.rangeres_ids, self.rangeres_dists) = self.index.range_search( | ||
| X, dim, nq, radius, self.para) | ||
|
|
||
| def get_results(self): | ||
| """ | ||
| Helper method to convert query results of k-NN search. | ||
| If there are nq queries, returns a (nq, k) array of integers | ||
| representing the indices of the k-NN for each query. | ||
| """ | ||
| return self.res | ||
|
|
||
| def get_range_results(self): | ||
| """ | ||
| Helper method to convert query results of range search. | ||
| If there are nq queries, returns a triple lims, I, D. | ||
| lims is a (nq) array, such that | ||
| I[lims[q]:lims[q + 1]] in int | ||
| are the indiices of the indices of the range results of query q, and | ||
| D[lims[q]:lims[q + 1]] in float | ||
| are the distances. | ||
| -------------------------- | ||
| NEED TO CHECK THE ORDER of I and D in plot.py/ utils.py. | ||
| -------------------------- | ||
| """ | ||
| return (self.rangeres_lim, self.rangeres_dists, self.rangeres_ids) | ||
|
|
||
| def get_additional(self): | ||
| """ | ||
| Allows to retrieve additional results. | ||
| """ | ||
| return {} | ||
|
|
||
| def __str__(self): | ||
| return self.name | ||
|
|
||
| def get_memory_usage(self): | ||
| """Return the current memory usage of this algorithm instance | ||
| (in kilobytes), or None if this information is not available.""" | ||
| # return in kB for backwards compatibility | ||
| return psutil.Process().memory_info().rss / 1024 |
Oops, something went wrong.