File size: 7,696 Bytes
03baa4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
import argparse
import copy
import logging
import logging.handlers as handlers
import pathlib
import sys
import faiss
import numpy as np
import vaex as vx
import wandb
sys.path.insert(0, str(pathlib.Path(__file__).parent.resolve()))
from search.embeddings import Embeddings
from search.faiss_search import FaissIndex
from metrics import metrics
from data.wikiart import WikiArt, MyTrain
logger = logging.getLogger()
def get_parser():
parser = argparse.ArgumentParser('dynamicDistances-NN Search Module')
parser.add_argument('--dataset', default='wikiart', type=str, required=True)
parser.add_argument('--topk', nargs='+', type=int, default=[5],
help='Number of NN to consider while calculating recall')
parser.add_argument('--mode', type=str, required=True, choices=['artist', 'label'],
help='The type of matching to do')
parser.add_argument('--method', type=str, default='IP', choices=['IP', 'L2'], help='The method to do NN search')
parser.add_argument('--emb-dir', type=str, default=None,
help='The directory where per image embeddings are stored (NOT USED when chunked)')
parser.add_argument('--query_count', default=-1, type=int,
help='Number of queries to consider. Works only for domainnet')
parser.add_argument('--chunked', action='store_true', help='If I should read from chunked directory instead')
parser.add_argument('--query-chunk-dir', type=str, required=True,
help='The directory where chunked query embeddings should be saved/are already saved')
parser.add_argument('--database-chunk-dir', type=str, required=True,
help='The directory where chunked val embeddings should be saved/are already saved')
parser.add_argument('--data-dir', type=str, default=None,
help='The directory of concerned dataset. (HARD CODED LATER)')
parser.add_argument('--multilabel', action='store_true', help='If the dataset is multilabel')
return parser
def get_log_handlers(args):
# Create handlers
c_handler = logging.StreamHandler()
f_handler = handlers.RotatingFileHandler(f'search.log', maxBytes=int(1e6), backupCount=1000)
c_handler.setLevel(logging.DEBUG)
f_handler.setLevel(logging.DEBUG)
# Create formatters and add it to handlers
c_format = logging.Formatter('%(name)s - %(levelname)s - %(message)s')
f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
c_handler.setFormatter(c_format)
f_handler.setFormatter(f_format)
return c_handler, f_handler
def main():
parser = get_parser()
args = parser.parse_args()
handlers = get_log_handlers(args)
logger.addHandler(handlers[0])
logger.addHandler(handlers[1])
logger.setLevel(logging.DEBUG)
if args.dataset == 'wikiart':
dataset = WikiArt(args.data_dir)
else:
raise NotImplementedError
query_embeddings = Embeddings(args.emb_dir, args.query_chunk_dir,
files=None,
chunked=args.chunked,
file_ext='.pkl')
val_embeddings = Embeddings(args.emb_dir, args.database_chunk_dir,
files=None,
chunked=args.chunked,
file_ext='.pkl')
query_embeddings.filenames = list(query_embeddings.filenames)
val_embeddings.filenames = list(val_embeddings.filenames)
# Filtering the dataset based on the files which actually exist.
dataset.query_db = dataset.query_db[
dataset.query_db['name'].isin(query_embeddings.filenames)]
dataset.val_db = dataset.val_db[
dataset.val_db['name'].isin(val_embeddings.filenames)]
# Using only the embeddings corresponding to images in the datasets
temp = vx.from_arrays(filename=query_embeddings.filenames, index=np.arange(len(query_embeddings.filenames)))
print(temp)
dataset.query_db = dataset.query_db.join(temp, left_on='name', right_on='filename', how='left')
query_embeddings.embeddings = query_embeddings.embeddings[dataset.get_query_col('index')]
try:
b, h, w = query_embeddings.embeddings.shape
query_embeddings.embeddings = query_embeddings.embeddings.reshape(b, 1, h * w)
except ValueError:
b, d = query_embeddings.embeddings.shape
query_embeddings.embeddings = query_embeddings.embeddings.reshape(b, 1, d)
query_embeddings.filenames = np.asarray(query_embeddings.filenames)[dataset.get_query_col('index')]
temp = vx.from_arrays(filename=val_embeddings.filenames, index=np.arange(len(val_embeddings.filenames)))
dataset.val_db = dataset.val_db.join(temp, left_on='name', right_on='filename', how='left')
val_embeddings.embeddings = val_embeddings.embeddings[dataset.get_val_col('index')]
try:
b, h, w = val_embeddings.embeddings.shape
val_embeddings.embeddings = val_embeddings.embeddings.reshape(b, 1, h * w)
except ValueError:
b, d = val_embeddings.embeddings.shape
val_embeddings.embeddings = val_embeddings.embeddings.reshape(b, 1, d)
val_embeddings.filenames = np.asarray(val_embeddings.filenames)[dataset.get_val_col('index')]
# Building the faiss index
embedding_size = query_embeddings.embeddings[0].shape[1]
if args.method == 'IP':
method = faiss.IndexFlatIP
else:
method = faiss.IndexFlatL2
search_module = FaissIndex(embedding_size=embedding_size, index_func=method)
queries = np.asarray(query_embeddings.embeddings).reshape(len(query_embeddings.embeddings), embedding_size)
database = np.asarray(val_embeddings.embeddings).reshape(len(val_embeddings.embeddings), embedding_size)
search_module.build_index(database)
_, nns_all = search_module.search_nns(queries, max(args.topk))
if args.multilabel:
q_labels = dataset.query_db['multilabel'].values
db_labels = dataset.val_db['multilabel'].values
nns_all_pred = [q_labels[i] @ db_labels[nns_all[i]].T for i in range(len(nns_all))] # (n_queries, n_topk)
nns_all_pred = np.array(nns_all_pred)
else:
nns_all_pred = nns_all
classes = np.unique(dataset.get_val_col(args.mode))
mode_to_index = {classname: i for i, classname in enumerate(classes)}
try:
gts = np.asarray(list(map(lambda x: mode_to_index[x], dataset.get_query_col(args.mode).tolist())))
except KeyError:
logger.error('Class not found in database. This query list cannot be evaluated')
return
evals = metrics.Metrics()
for topk in args.topk:
logger.info(f'Calculating recall@{topk}')
nns_all_pred_topk = nns_all_pred[:, :topk]
if args.multilabel:
mode_recall = evals.get_recall_bin(copy.deepcopy(nns_all_pred_topk), topk)
mode_mrr = evals.get_mrr_bin(copy.deepcopy(nns_all_pred_topk), topk)
mode_map = evals.get_map_bin(copy.deepcopy(nns_all_pred_topk), topk)
else:
preds = dataset.get_val_col(args.mode)[nns_all_pred_topk.flatten()].reshape(len(queries), topk)
preds = np.vectorize(mode_to_index.get)(preds)
mode_recall = evals.get_recall(copy.deepcopy(preds), gts, topk)
mode_mrr = evals.get_mrr(copy.deepcopy(preds), gts, topk)
mode_map = evals.get_map(copy.deepcopy(preds), gts, topk)
logger.info(f'Recall@{topk}: {mode_recall}')
logger.info(f'MRR@{topk}: {mode_mrr}')
logger.info(f'mAP@{topk}: {mode_map}')
if __name__ == '__main__':
main()
|