Datasets:
Tasks:
Object Detection
Formats:
webdataset
Languages:
English
Size:
< 1K
ArXiv:
Tags:
webdataset
License:
File size: 2,590 Bytes
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import faiss
import numpy as np
import torch
import os, glob
def get_results(features_path):
print(features_path)
embeddings_np = torch.load(features_path).numpy()
all_cow_ids = torch.load("../big_model_inference/all_cow_ids.pt").numpy()
mid_point = len(embeddings_np) // 2
# print(f"mid_point : {mid_point}")
embeddings_np_first_half = embeddings_np[:mid_point]
embeddings_np_second_half = embeddings_np[mid_point:]
all_cow_ids_first_half = all_cow_ids[:mid_point]
all_cow_ids_second_half = all_cow_ids[mid_point:]
# # Assuming embeddings_np is your numpy array of shape (N, 512) and dtype=np.float32
d = embeddings_np_first_half.shape[1] # Dimensionality (512)
nlist = 100 # Number of clusters (you can tune this parameter)
m = 8 # Number of subquantizers (must be a divisor of d)
nbits = 8 # Bits per subquantizer
flat_index = faiss.IndexFlatL2(d)
index_ivf = faiss.IndexIVFPQ(flat_index, d, nlist, m, nbits)
index_ivf.nprobe = 10
index_ivf.train(embeddings_np_first_half)
index_ivf.add(embeddings_np_first_half)
# flat_index.add(embeddings_np_first_half)
k = 6
distances, indices = index_ivf.search(embeddings_np_second_half, k)
# print("Nearest neighbors (indices) for the first 10 images:")
# print(indices[-10:])
# print("Corresponding distances:")
# print(distances[-10:])
# Calculate top-1 and top-5 accuracy
top1_correct = 0
top5_correct = 0
for i, indices_row in enumerate(indices):
query_id = all_cow_ids_second_half[i]
# Get cow IDs for the retrieved results
retrieved_ids = [all_cow_ids_first_half[idx] for idx in indices_row]
# Top-1: Check if the first result matches the query ID
if retrieved_ids[0] == query_id:
top1_correct += 1
# Top-5: Check if any of the first 5 results match the query ID
if query_id in retrieved_ids[:5]:
top5_correct += 1
# Calculate accuracy rates
top1_accuracy = top1_correct / len(embeddings_np_second_half)
top5_accuracy = top5_correct / len(embeddings_np_second_half)
print(f"Top-1 Accuracy: {top1_accuracy:.4f}")
print(f"Top-5 Accuracy: {top5_accuracy:.4f}")
directory = '../big_model_inference' # replace with your directory path
pattern = os.path.join(directory, '*.pt')
exclude_file = 'all_cow_ids.pt'
for features_path in glob.glob(pattern):
if os.path.basename(features_path) != exclude_file:
get_results(features_path)
# print(features_path) |