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# -*- coding: utf-8 -*-
'''
This scripts performs kNN search on inferenced image and text features (on single-GPU) and outputs text-to-image prediction file for evaluation.
'''
import argparse
import numpy
from tqdm import tqdm
import json
import numpy as np
import torch
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--image-feats',
type=str,
required=True,
help="Specify the path of image features."
)
parser.add_argument(
'--text-feats',
type=str,
required=True,
help="Specify the path of text features."
)
parser.add_argument(
'--top-k',
type=int,
default=10,
help="Specify the k value of top-k predictions."
)
parser.add_argument(
'--eval-batch-size',
type=int,
default=32768,
help="Specify the image-side batch size when computing the inner products, default to 8192"
)
parser.add_argument(
'--output',
type=str,
required=True,
help="Specify the output jsonl prediction filepath."
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# Log params.
print("Params:")
for name in sorted(vars(args)):
val = getattr(args, name)
print(f" {name}: {val}")
print("Begin to load image features...")
image_ids = []
image_feats = []
with open(args.image_feats, "r") as fin:
for line in tqdm(fin):
obj = json.loads(line.strip())
image_ids.append(obj['image_id'])
image_feats.append(obj['feature'])
image_feats_array = np.array(image_feats, dtype=np.float32)
print("Finished loading image features.")
print("Begin to compute top-{} predictions for texts...".format(args.top_k))
with open(args.output, "w") as fout:
with open(args.text_feats, "r") as fin:
for line in tqdm(fin):
obj = json.loads(line.strip())
text_id = obj['text_id']
text_feat = obj['feature']
score_tuples = []
text_feat_tensor = torch.tensor([text_feat], dtype=torch.float).cuda() # [1, feature_dim]
idx = 0
while idx < len(image_ids):
img_feats_tensor = torch.from_numpy(image_feats_array[idx : min(idx + args.eval_batch_size, len(image_ids))]).cuda() # [batch_size, feature_dim]
batch_scores = text_feat_tensor @ img_feats_tensor.t() # [1, batch_size]
for image_id, score in zip(image_ids[idx : min(idx + args.eval_batch_size, len(image_ids))], batch_scores.squeeze(0).tolist()):
score_tuples.append((image_id, score))
idx += args.eval_batch_size
top_k_predictions = sorted(score_tuples, key=lambda x:x[1], reverse=True)[:args.top_k]
fout.write("{}\n".format(json.dumps({"text_id": text_id, "image_ids": [entry[0] for entry in top_k_predictions]})))
print("Top-{} predictions are saved in {}".format(args.top_k, args.output))
print("Done!")
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