mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 07:54:07 +00:00
WiP refactoring niraj's task 4, some bugfixes
This commit is contained in:
parent
e92e324065
commit
b935d9ca34
@ -10,4 +10,5 @@ pymongo[srv]
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ipython
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ipython
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notebook
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notebook
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ipykernel
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ipykernel
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python-dotenv
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python-dotenv
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tensorly
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52
Phase 2/task_4.ipynb
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52
Phase 2/task_4.ipynb
Normal file
@ -0,0 +1,52 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import *\n",
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"warnings.filterwarnings('ignore')\n",
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"%matplotlib inline\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -22,124 +22,127 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 7,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n",
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"Applying kmeans on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n",
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"Initialized centroids\n",
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"Iteration 6 - Converged\n",
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"Note: for K-Means we display distances, in ascending order\n",
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"Latent semantic no. 0\n",
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"Latent semantic no. 0\n",
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"label\t28\t-\tWeight\t0.2583354411312026\n",
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"label\t92\t-\tDistance\t3.230292112512146\n",
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"label\t29\t-\tWeight\t0.2301362547676974\n",
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"label\t4\t-\tDistance\t3.5335656340201087\n",
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"label\t33\t-\tWeight\t0.2129183683279978\n",
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"label\t2\t-\tDistance\t4.905027845590568\n",
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"label\t9\t-\tWeight\t0.17625685452423093\n",
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"label\t69\t-\tDistance\t4.993399423965622\n",
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"label\t95\t-\tWeight\t0.16277551497836534\n",
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"label\t65\t-\tDistance\t6.275170101152081\n",
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"label\t47\t-\tWeight\t0.1424860388015467\n",
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"label\t21\t-\tDistance\t6.792963383606834\n",
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"label\t39\t-\tWeight\t0.1349747704005884\n",
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"label\t95\t-\tDistance\t9.460863854781731\n",
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"label\t30\t-\tWeight\t0.13251434767496492\n",
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"label\t60\t-\tDistance\t10.659440914917885\n",
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"label\t52\t-\tWeight\t0.12669069496270755\n",
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"label\t82\t-\tDistance\t14.23961431596092\n",
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"label\t8\t-\tWeight\t0.1257730807471899\n",
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"label\t51\t-\tDistance\t14.308250416010853\n",
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"Latent semantic no. 1\n",
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"Latent semantic no. 1\n",
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"label\t96\t-\tWeight\t0.2666765976054894\n",
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"label\t47\t-\tDistance\t1.7917105751649582\n",
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"label\t97\t-\tWeight\t0.19087869496500426\n",
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"label\t42\t-\tDistance\t1.8293437639389183\n",
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"label\t25\t-\tWeight\t0.17776094778851348\n",
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"label\t35\t-\tDistance\t2.47989550940933\n",
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"label\t3\t-\tWeight\t0.1759798805642099\n",
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"label\t29\t-\tDistance\t2.4870731532031694\n",
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"label\t98\t-\tWeight\t0.16951497899752574\n",
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"label\t33\t-\tDistance\t3.0078415187323975\n",
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"label\t22\t-\tWeight\t0.1667032655640346\n",
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"label\t49\t-\tDistance\t3.1694527370940753\n",
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"label\t24\t-\tWeight\t0.16034180060184824\n",
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"label\t54\t-\tDistance\t3.1764161450515775\n",
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"label\t19\t-\tWeight\t0.15345532912389587\n",
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"label\t28\t-\tDistance\t3.520891544025031\n",
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"label\t52\t-\tWeight\t0.13271640119612757\n",
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"label\t19\t-\tDistance\t3.752147129401601\n",
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"label\t29\t-\tWeight\t0.12856388746021633\n",
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"label\t82\t-\tDistance\t3.9820650145644705\n",
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"Latent semantic no. 2\n",
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"Latent semantic no. 2\n",
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"label\t46\t-\tWeight\t0.21813474254675366\n",
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"label\t91\t-\tDistance\t2.7653145272024493\n",
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"label\t79\t-\tWeight\t0.19091788352587957\n",
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"label\t14\t-\tDistance\t3.829858168383929\n",
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"label\t55\t-\tWeight\t0.1871080482210247\n",
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"label\t93\t-\tDistance\t4.108580770102051\n",
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"label\t56\t-\tWeight\t0.18322792605578184\n",
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"label\t48\t-\tDistance\t4.25643528657963\n",
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"label\t78\t-\tWeight\t0.17506936966351683\n",
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"label\t85\t-\tDistance\t4.308356278561495\n",
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"label\t98\t-\tWeight\t0.1733164832137484\n",
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"label\t17\t-\tDistance\t4.72066235395654\n",
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"label\t22\t-\tWeight\t0.17114312653027375\n",
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"label\t52\t-\tDistance\t4.733719921198274\n",
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"label\t38\t-\tWeight\t0.16928636840289424\n",
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"label\t43\t-\tDistance\t5.593133775346241\n",
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"label\t45\t-\tWeight\t0.1567042877228484\n",
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"label\t75\t-\tDistance\t6.35213810417939\n",
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"label\t4\t-\tWeight\t0.15108693899889344\n",
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"label\t83\t-\tDistance\t6.365421291009637\n",
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"Latent semantic no. 3\n",
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"Latent semantic no. 3\n",
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"label\t96\t-\tWeight\t0.2736613529052896\n",
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"label\t63\t-\tDistance\t3.0750924250527425\n",
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"label\t98\t-\tWeight\t0.218185914155306\n",
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"label\t98\t-\tDistance\t3.256907164618595\n",
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"label\t22\t-\tWeight\t0.1963451355822489\n",
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"label\t59\t-\tDistance\t3.36740335111714\n",
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"label\t3\t-\tWeight\t0.17627732148468614\n",
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"label\t32\t-\tDistance\t3.4369727667587036\n",
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"label\t39\t-\tWeight\t0.1728992502839298\n",
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"label\t84\t-\tDistance\t4.042695694344645\n",
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"label\t52\t-\tWeight\t0.15597562436756945\n",
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"label\t79\t-\tDistance\t4.051227266452548\n",
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"label\t51\t-\tWeight\t0.1291470561734402\n",
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"label\t94\t-\tDistance\t4.535286748567164\n",
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"label\t30\t-\tWeight\t0.12453129554714541\n",
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"label\t75\t-\tDistance\t4.567193344282598\n",
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"label\t18\t-\tWeight\t0.1236867360720947\n",
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"label\t11\t-\tDistance\t4.856460310962189\n",
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"label\t38\t-\tWeight\t0.12184856229773917\n",
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"label\t55\t-\tDistance\t5.036016117772108\n",
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"Latent semantic no. 4\n",
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"Latent semantic no. 4\n",
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"label\t6\t-\tWeight\t0.23875690719216863\n",
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"label\t80\t-\tDistance\t4.403201299886196\n",
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"label\t67\t-\tWeight\t0.21007869938490106\n",
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"label\t99\t-\tDistance\t4.731021526243766\n",
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"label\t63\t-\tWeight\t0.18822840034389135\n",
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"label\t3\t-\tDistance\t4.807090489912411\n",
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"label\t14\t-\tWeight\t0.18738002200878218\n",
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"label\t48\t-\tDistance\t8.911953449338059\n",
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"label\t87\t-\tWeight\t0.17508576062247283\n",
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"label\t85\t-\tDistance\t9.334554754293974\n",
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"label\t23\t-\tWeight\t0.167492867766091\n",
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"label\t52\t-\tDistance\t11.390353342613288\n",
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"label\t15\t-\tWeight\t0.15522709562173342\n",
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"label\t43\t-\tDistance\t12.033766054009595\n",
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"label\t61\t-\tWeight\t0.13244353806854162\n",
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"label\t91\t-\tDistance\t12.446673116679838\n",
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"label\t45\t-\tWeight\t0.12833204093005665\n",
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"label\t14\t-\tDistance\t12.717196488491759\n",
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"label\t68\t-\tWeight\t0.12622315521729294\n",
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"label\t83\t-\tDistance\t13.5754060440636\n",
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"Latent semantic no. 5\n",
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"Latent semantic no. 5\n",
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"label\t30\t-\tWeight\t0.17385975982344382\n",
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"label\t77\t-\tDistance\t2.144778050426236\n",
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"label\t25\t-\tWeight\t0.14655711054814133\n",
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"label\t45\t-\tDistance\t2.3391902699042175\n",
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"label\t39\t-\tWeight\t0.13307896633493813\n",
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"label\t73\t-\tDistance\t2.5586280095180554\n",
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"label\t68\t-\tWeight\t0.12851498788897622\n",
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"label\t22\t-\tDistance\t2.833603911721891\n",
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"label\t24\t-\tWeight\t0.12828250585375986\n",
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"label\t57\t-\tDistance\t2.9256965790964955\n",
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"label\t0\t-\tWeight\t0.12500243174429157\n",
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"label\t50\t-\tDistance\t3.216841848641699\n",
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"label\t1\t-\tWeight\t0.12371257574727512\n",
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"label\t74\t-\tDistance\t3.2964675276683377\n",
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"label\t77\t-\tWeight\t0.12370279647800499\n",
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"label\t38\t-\tDistance\t3.3501016749777297\n",
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"label\t89\t-\tWeight\t0.12233344688386875\n",
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"label\t72\t-\tDistance\t3.461208008080578\n",
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"label\t83\t-\tWeight\t0.11445596984835589\n",
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"label\t34\t-\tDistance\t3.8970766980234073\n",
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"Latent semantic no. 6\n",
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"Latent semantic no. 6\n",
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"label\t17\t-\tWeight\t0.2335282879255542\n",
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"label\t78\t-\tDistance\t1.772794735295686\n",
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"label\t48\t-\tWeight\t0.19418795795666355\n",
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"label\t6\t-\tDistance\t1.9243189269571448\n",
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"label\t21\t-\tWeight\t0.19013440200231033\n",
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"label\t67\t-\tDistance\t2.0159218514234905\n",
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"label\t85\t-\tWeight\t0.17503295059460947\n",
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"label\t23\t-\tDistance\t2.0402136200750687\n",
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"label\t11\t-\tWeight\t0.14933372636956993\n",
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"label\t7\t-\tDistance\t2.1597363741525943\n",
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"label\t1\t-\tWeight\t0.1384254243377172\n",
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"label\t15\t-\tDistance\t2.2890961861911463\n",
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"label\t0\t-\tWeight\t0.13078647401074162\n",
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"label\t86\t-\tDistance\t2.418355035843437\n",
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"label\t57\t-\tWeight\t0.11374248801163754\n",
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"label\t39\t-\tDistance\t2.431493894783776\n",
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"label\t10\t-\tWeight\t0.10468223841103744\n",
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"label\t20\t-\tDistance\t2.4339361855736694\n",
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"label\t99\t-\tWeight\t0.10191451131216464\n",
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"label\t61\t-\tDistance\t2.4663666328704577\n",
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"Latent semantic no. 7\n",
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"Latent semantic no. 7\n",
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"label\t82\t-\tWeight\t0.23372455436757703\n",
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"label\t36\t-\tDistance\t2.148560462001178\n",
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"label\t95\t-\tWeight\t0.21795238756371887\n",
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"label\t10\t-\tDistance\t2.336732460490279\n",
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"label\t60\t-\tWeight\t0.18080422229063045\n",
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"label\t76\t-\tDistance\t2.410558517560451\n",
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"label\t16\t-\tWeight\t0.1806105172209771\n",
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"label\t9\t-\tDistance\t2.4853810228702433\n",
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"label\t27\t-\tWeight\t0.17365150902149876\n",
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"label\t44\t-\tDistance\t2.822322732248757\n",
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"label\t59\t-\tWeight\t0.17250044548228938\n",
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"label\t16\t-\tDistance\t2.8525379488476954\n",
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"label\t26\t-\tWeight\t0.1661853291143862\n",
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"label\t19\t-\tDistance\t2.887333058828606\n",
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"label\t13\t-\tWeight\t0.16331211225170805\n",
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"label\t41\t-\tDistance\t3.2609266747980072\n",
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"label\t34\t-\tWeight\t0.1523080193090529\n",
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"label\t0\t-\tDistance\t3.4462772872176073\n",
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"label\t67\t-\tWeight\t0.13577900574984025\n",
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"label\t8\t-\tDistance\t3.4492972662700305\n",
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"Latent semantic no. 8\n",
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"Latent semantic no. 8\n",
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"label\t53\t-\tWeight\t0.2259481751468642\n",
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"label\t60\t-\tDistance\t3.2878466679861047\n",
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"label\t37\t-\tWeight\t0.21583443408756542\n",
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"label\t66\t-\tDistance\t3.8429959542446595\n",
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"label\t76\t-\tWeight\t0.20483376297311964\n",
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"label\t95\t-\tDistance\t4.407501055402251\n",
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"label\t44\t-\tWeight\t0.1690198227623472\n",
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"label\t51\t-\tDistance\t4.675169110980285\n",
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"label\t68\t-\tWeight\t0.1650723880318989\n",
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"label\t82\t-\tDistance\t4.930711123344968\n",
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"label\t28\t-\tWeight\t0.15689929414378492\n",
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"label\t1\t-\tDistance\t5.746326956457264\n",
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"label\t14\t-\tWeight\t0.1564371673909956\n",
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"label\t42\t-\tDistance\t5.932080034810729\n",
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"label\t54\t-\tWeight\t0.1553627917623035\n",
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"label\t29\t-\tDistance\t5.934164464898548\n",
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"label\t51\t-\tWeight\t0.14380435363337046\n",
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"label\t47\t-\tDistance\t6.3479330191887025\n",
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"label\t36\t-\tWeight\t0.13510425005259438\n",
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"label\t35\t-\tDistance\t6.422013021100036\n",
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"Latent semantic no. 9\n",
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"Latent semantic no. 9\n",
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"label\t19\t-\tWeight\t0.11741024839079275\n",
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"label\t83\t-\tDistance\t5.036696108166727\n",
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"label\t40\t-\tWeight\t0.11107319334138463\n",
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"label\t100\t-\tDistance\t5.163440732380748\n",
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"label\t53\t-\tWeight\t0.11058750626248925\n",
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"label\t43\t-\tDistance\t5.447889420797845\n",
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"label\t51\t-\tWeight\t0.10794606425819818\n",
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"label\t88\t-\tDistance\t6.470159759945887\n",
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"label\t96\t-\tWeight\t0.10735468567860716\n",
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"label\t64\t-\tDistance\t6.8077571085247355\n",
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"label\t55\t-\tWeight\t0.10731282010915796\n",
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"label\t17\t-\tDistance\t7.350448996699054\n",
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"label\t50\t-\tWeight\t0.10703093662670059\n",
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"label\t55\t-\tDistance\t7.555979165305925\n",
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"label\t1\t-\tWeight\t0.10651036503732043\n",
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"label\t11\t-\tDistance\t7.84770773092541\n",
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"label\t79\t-\tWeight\t0.10640855392103846\n",
|
"label\t91\t-\tDistance\t7.869761874577601\n",
|
||||||
"label\t47\t-\tWeight\t0.10594110421348357\n"
|
"label\t75\t-\tDistance\t7.997112142085329\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
|||||||
@ -18,6 +18,8 @@ import torchvision.transforms as transforms
|
|||||||
from torchvision.datasets import Caltech101
|
from torchvision.datasets import Caltech101
|
||||||
from torchvision.models import resnet50, ResNet50_Weights
|
from torchvision.models import resnet50, ResNet50_Weights
|
||||||
|
|
||||||
|
import tensorly as tl
|
||||||
|
|
||||||
# OS and env
|
# OS and env
|
||||||
import json
|
import json
|
||||||
from os import getenv
|
from os import getenv
|
||||||
@ -58,6 +60,8 @@ def loadDataset(dataset):
|
|||||||
|
|
||||||
|
|
||||||
dataset = loadDataset(Caltech101)
|
dataset = loadDataset(Caltech101)
|
||||||
|
NUM_LABELS = 101
|
||||||
|
NUM_IMAGES = 4339
|
||||||
|
|
||||||
|
|
||||||
class GridPartition:
|
class GridPartition:
|
||||||
@ -998,7 +1002,7 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
"semantic-feature": V_T.tolist(),
|
"semantic-feature": V_T.tolist(),
|
||||||
}
|
}
|
||||||
|
|
||||||
# for each latent semantic, sort imageID-weight pairs by weights in descending order
|
# for each latent semantic, sort object-weight pairs by weights in descending order
|
||||||
displayed_latent_semantics = [
|
displayed_latent_semantics = [
|
||||||
sorted(
|
sorted(
|
||||||
list(zip(feature_ids, latent_semantic)),
|
list(zip(feature_ids, latent_semantic)),
|
||||||
@ -1033,7 +1037,7 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
"semantic-feature": H.tolist(),
|
"semantic-feature": H.tolist(),
|
||||||
}
|
}
|
||||||
|
|
||||||
# for each latent semantic, sort imageID-weight pairs by weights in descending order
|
# for each latent semantic, sort object-weight pairs by weights in descending order
|
||||||
displayed_latent_semantics = [
|
displayed_latent_semantics = [
|
||||||
sorted(
|
sorted(
|
||||||
list(zip(feature_ids, latent_semantic)),
|
list(zip(feature_ids, latent_semantic)),
|
||||||
@ -1066,7 +1070,7 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
"semantic-feature": K.tolist(),
|
"semantic-feature": K.tolist(),
|
||||||
}
|
}
|
||||||
|
|
||||||
# for each latent semantic, sort imageID-weight pairs by weights in descending order
|
# for each latent semantic, sort object-weight pairs by weights in descending order
|
||||||
displayed_latent_semantics = [
|
displayed_latent_semantics = [
|
||||||
sorted(
|
sorted(
|
||||||
list(zip(feature_ids, latent_semantic)),
|
list(zip(feature_ids, latent_semantic)),
|
||||||
@ -1084,10 +1088,10 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
|
|
||||||
all_latent_semantics = {
|
all_latent_semantics = {
|
||||||
"image-semantic": Y.tolist(),
|
"image-semantic": Y.tolist(),
|
||||||
"semantic-feature": list(CC.values()),
|
"semantic-feature": CC.tolist(),
|
||||||
}
|
}
|
||||||
|
|
||||||
# for each latent semantic, sort imageID-weight pairs by weights in descending order
|
# for each latent semantic, sort object-weight pairs by weights in ascending order
|
||||||
displayed_latent_semantics = [
|
displayed_latent_semantics = [
|
||||||
sorted(
|
sorted(
|
||||||
list(zip(feature_ids, latent_semantic)),
|
list(zip(feature_ids, latent_semantic)),
|
||||||
@ -1096,9 +1100,8 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
)[:top_images]
|
)[:top_images]
|
||||||
for latent_semantic in Y.T
|
for latent_semantic in Y.T
|
||||||
]
|
]
|
||||||
|
print("Note: for K-Means we display distances, in ascending order")
|
||||||
|
|
||||||
if valid_dim_reduction_methods[dim_reduction_method] == 4:
|
|
||||||
print("Note: for K-Means we display distances, in ascending order")
|
|
||||||
for idx, latent_semantic in enumerate(displayed_latent_semantics):
|
for idx, latent_semantic in enumerate(displayed_latent_semantics):
|
||||||
print(f"Latent semantic no. {idx}")
|
print(f"Latent semantic no. {idx}")
|
||||||
for obj_id, weight in latent_semantic:
|
for obj_id, weight in latent_semantic:
|
||||||
@ -1129,19 +1132,17 @@ def find_label_label_similarity(fd_collection, feature_model):
|
|||||||
label_sim_matrix = []
|
label_sim_matrix = []
|
||||||
label_mean_vectors = []
|
label_mean_vectors = []
|
||||||
|
|
||||||
num_labels = 101
|
for label in range(NUM_LABELS):
|
||||||
|
|
||||||
for label in range(num_labels):
|
|
||||||
# get representative vectors for the label
|
# get representative vectors for the label
|
||||||
label_mean_vectors.append(
|
label_mean_vectors.append(
|
||||||
calculate_label_representatives(fd_collection, label, feature_model)
|
calculate_label_representatives(fd_collection, label, feature_model)
|
||||||
)
|
)
|
||||||
|
|
||||||
label_sim_matrix = np.zeros((num_labels, num_labels))
|
label_sim_matrix = np.zeros((NUM_LABELS, NUM_LABELS))
|
||||||
|
|
||||||
# Calculate half and fill the other
|
# Calculate half and fill the other
|
||||||
for i in range(num_labels):
|
for i in range(NUM_LABELS):
|
||||||
for j in range(i + 1, num_labels):
|
for j in range(i + 1, NUM_LABELS):
|
||||||
# Note: lower the value, lower the distance => higher the similarity
|
# Note: lower the value, lower the distance => higher the similarity
|
||||||
label_sim_matrix[i][j] = label_sim_matrix[j][i] = feature_distance_matches[
|
label_sim_matrix[i][j] = label_sim_matrix[j][i] = feature_distance_matches[
|
||||||
feature_model
|
feature_model
|
||||||
@ -1163,14 +1164,30 @@ def find_image_image_similarity(fd_collection, feature_model):
|
|||||||
).flatten() # get the specific feature model's feature vector
|
).flatten() # get the specific feature model's feature vector
|
||||||
for img_fds in fd_collection.find() # repeat for all images
|
for img_fds in fd_collection.find() # repeat for all images
|
||||||
]
|
]
|
||||||
num_images = len(feature_vectors)
|
image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))
|
||||||
image_sim_matrix = np.zeros((num_images, num_images))
|
|
||||||
|
|
||||||
# Calculate half and fill the other
|
# Calculate half and fill the other
|
||||||
for i in range(num_images):
|
for i in range(NUM_IMAGES):
|
||||||
for j in range(i + 1, num_images):
|
for j in range(i + 1, NUM_IMAGES):
|
||||||
# Note: lower the value, lower the distance => higher the similarity
|
# Note: lower the value, lower the distance => higher the similarity
|
||||||
image_sim_matrix[i][j] = image_sim_matrix[j][i] = feature_distance_matches[
|
image_sim_matrix[i][j] = image_sim_matrix[j][i] = feature_distance_matches[
|
||||||
feature_model
|
feature_model
|
||||||
](np.array(feature_vectors[i]), np.array(feature_vectors[j]))
|
](np.array(feature_vectors[i]), np.array(feature_vectors[j]))
|
||||||
return image_sim_matrix
|
return image_sim_matrix
|
||||||
|
|
||||||
|
def compute_cp_decomposition(fd_collection, feature_model, rank):
|
||||||
|
assert (
|
||||||
|
feature_model in valid_feature_models.values()
|
||||||
|
), "feature_model should be one of " + str(list(valid_feature_models.keys()))
|
||||||
|
|
||||||
|
all_images = list(fd_collection.find())
|
||||||
|
|
||||||
|
# (images, features, labels)
|
||||||
|
data_tensor_shape = (NUM_IMAGES, len(all_images[0][feature_model]), NUM_LABELS)
|
||||||
|
data_tensor = np.zeros(data_tensor_shape)
|
||||||
|
for id in range(NUM_IMAGES):
|
||||||
|
label = all_images[id]["true_label"]
|
||||||
|
data_tensor[id, :, label] = all_images[id][feature_model]
|
||||||
|
|
||||||
|
weights_tensor, factor_matrices = tl.decomposition.parafac(data_tensor, rank=rank, normalize_factors=True)
|
||||||
|
return weights_tensor, factor_matrices
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user