mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 11:04:07 +00:00
Merge branch 'master' of https://github.com/20kaushik02/CSE515_MWDB_Project into task9
This commit is contained in:
pranavbrkr
commit
950d4237b3
@ -11,3 +11,4 @@ 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
Normal file
52
Phase 2/task_4.ipynb
Normal file
@ -0,0 +1,52 @@
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{
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||||||
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"cells": [
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{
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||||||
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"cell_type": "code",
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||||||
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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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64
Phase 2/task_4.py
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64
Phase 2/task_4.py
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@ -0,0 +1,64 @@
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import json
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import tensorly as tl
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import numpy as np
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from pymongo import MongoClient
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from phase1_mongodb import *
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client = MongoClient()
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client = MongoClient(host="localhost", port=27017)
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# Select the database
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db = client.Multimedia_Web_DBs
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caltechDataset = loadDataset()
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num_labels = 101
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# Fetch all documents from the collection and then sort them by "_id"
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feature_descriptors = list(db.Feature_Descriptors.find({}))
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feature_descriptors = sorted(list(db.Feature_Descriptors.find({})), key=lambda x: x["_id"], reverse=False)
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label_ids = [x["label"] for x in feature_descriptors]
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def compute_cp_decomposition(feature_model, rank):
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label_vectors = [(x["label"], x[feature_model]) for x in feature_descriptors if x["_id"] % 2 == 0]
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num_labels = 101
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tensor_shape = (len(label_vectors), len(feature_descriptors[0][feature_model]), num_labels)
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tensor = np.zeros(tensor_shape)
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for id in range(len(label_vectors)):
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label = label_vectors[id][0]
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tensor[id, :, label] = label_vectors[id][1]
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weights, factors = tl.decomposition.parafac(tensor, rank=rank, normalize_factors=True)
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return weights, factors
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def main():
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# Step 4: Perform CP-decomposition (parafac) to extract latent semantics
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features = ['color_moments', 'hog', 'layer3', 'avgpool', 'fc']
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# User input for feature model to extract
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print("1: Color moments")
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print("2: HOG")
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print("3: Resnet50 Avgpool layer")
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print("4: Resnet50 Layer 3")
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print("5: Resnet50 FC layer")
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feature_model = features[int(input("Select the feature model: ")) - 1]
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k = int(input("Enter k: "))
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weights, factors = compute_cp_decomposition(feature_model, k)
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k_latent_semantics = list(zip(label_ids, factors[0].tolist()))
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k_latent_semantics_display = sorted(list(zip(label_ids, factors[0].tolist())), key = lambda x: x[1][0], reverse = True)
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k_latent_semantics_display = [{"_id": item[0], "semantics": item[1]} for item in k_latent_semantics_display]
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filename = f'{feature_model}-CP-semantics-{k}.json'
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k_latent_semantics = [{"_id": item[0], "semantics": item[1]} for item in k_latent_semantics]
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(k_latent_semantics, f, ensure_ascii = False)
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print(k_latent_semantics_display)
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if __name__ == "__main__":
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main()
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@ -2,7 +2,7 @@
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"cells": [
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"cells": [
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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": 19,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -13,7 +13,7 @@
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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": 20,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -22,159 +22,9 @@
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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": 21,
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"execution_count": null,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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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 0\n",
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"Iteration 1\n",
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"Iteration 2\n",
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"Iteration 3\n",
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"Iteration 4\n",
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"Iteration 5\n",
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"Iteration 6\n",
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"Iteration 7\n",
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"Iteration 8\n",
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"Iteration 9\n",
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"Iteration 10\n",
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"Iteration 11\n",
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"Iter 11 - Converged\n",
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"Latent semantic no. 0\n",
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"label\t84\t-\tWeight\t16.953715652557495\n",
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"label\t34\t-\tWeight\t17.25164883471016\n",
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"label\t1\t-\tWeight\t17.379970016799952\n",
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"label\t72\t-\tWeight\t17.439397073433092\n",
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"label\t32\t-\tWeight\t17.447297173030393\n",
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"label\t31\t-\tWeight\t17.448932606262144\n",
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"label\t40\t-\tWeight\t17.561159943630802\n",
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"label\t79\t-\tWeight\t17.572813876633166\n",
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"label\t5\t-\tWeight\t17.721278660592027\n",
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"label\t56\t-\tWeight\t17.731177291838822\n",
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"Latent semantic no. 1\n",
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"label\t84\t-\tWeight\t19.27643729221191\n",
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"label\t5\t-\tWeight\t19.449814613173483\n",
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"label\t32\t-\tWeight\t19.684592406270944\n",
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"label\t63\t-\tWeight\t19.911988624963808\n",
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"label\t79\t-\tWeight\t19.930151237028223\n",
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||||||
"label\t38\t-\tWeight\t19.948477661871497\n",
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"label\t89\t-\tWeight\t19.965086791647906\n",
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"label\t94\t-\tWeight\t19.990956583854018\n",
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"label\t72\t-\tWeight\t19.99680017871235\n",
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"label\t45\t-\tWeight\t20.058898160614795\n",
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"Latent semantic no. 2\n",
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"label\t0\t-\tWeight\tnan\n",
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"label\t1\t-\tWeight\tnan\n",
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"label\t2\t-\tWeight\tnan\n",
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"label\t3\t-\tWeight\tnan\n",
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"label\t4\t-\tWeight\tnan\n",
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"label\t5\t-\tWeight\tnan\n",
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"label\t6\t-\tWeight\tnan\n",
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"label\t7\t-\tWeight\tnan\n",
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"label\t8\t-\tWeight\tnan\n",
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"label\t9\t-\tWeight\tnan\n",
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"Latent semantic no. 3\n",
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"label\t0\t-\tWeight\tnan\n",
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"label\t1\t-\tWeight\tnan\n",
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"label\t2\t-\tWeight\tnan\n",
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"label\t3\t-\tWeight\tnan\n",
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"label\t4\t-\tWeight\tnan\n",
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"label\t5\t-\tWeight\tnan\n",
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"label\t6\t-\tWeight\tnan\n",
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"label\t7\t-\tWeight\tnan\n",
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"label\t8\t-\tWeight\tnan\n",
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"label\t9\t-\tWeight\tnan\n",
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"Latent semantic no. 4\n",
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"label\t32\t-\tWeight\t18.607843379925203\n",
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"label\t89\t-\tWeight\t18.671771165930238\n",
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||||||
"label\t84\t-\tWeight\t18.83858895833768\n",
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||||||
"label\t79\t-\tWeight\t18.84775924713071\n",
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"label\t55\t-\tWeight\t18.88614269359777\n",
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"label\t34\t-\tWeight\t18.891433443455583\n",
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||||||
"label\t11\t-\tWeight\t19.034715149675442\n",
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"label\t63\t-\tWeight\t19.042445031693624\n",
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"label\t5\t-\tWeight\t19.075471660855772\n",
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"label\t59\t-\tWeight\t19.096232354525338\n",
|
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"Latent semantic no. 5\n",
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"label\t88\t-\tWeight\t17.332684081151356\n",
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"label\t100\t-\tWeight\t17.414638052725692\n",
|
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||||||
"label\t89\t-\tWeight\t17.64193670680817\n",
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"label\t46\t-\tWeight\t17.663677856892257\n",
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"label\t5\t-\tWeight\t17.750606635854105\n",
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"label\t11\t-\tWeight\t17.921812162626082\n",
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"label\t17\t-\tWeight\t17.99728875058849\n",
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"label\t64\t-\tWeight\t18.20535869665654\n",
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"label\t84\t-\tWeight\t18.280826365832894\n",
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"label\t59\t-\tWeight\t18.48939095974247\n",
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"Latent semantic no. 6\n",
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"label\t0\t-\tWeight\tnan\n",
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"label\t1\t-\tWeight\tnan\n",
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"label\t2\t-\tWeight\tnan\n",
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"label\t3\t-\tWeight\tnan\n",
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"label\t4\t-\tWeight\tnan\n",
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"label\t5\t-\tWeight\tnan\n",
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"label\t6\t-\tWeight\tnan\n",
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"label\t7\t-\tWeight\tnan\n",
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"label\t8\t-\tWeight\tnan\n",
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"label\t9\t-\tWeight\tnan\n",
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"Latent semantic no. 7\n",
|
|
||||||
"label\t59\t-\tWeight\t19.676597202857955\n",
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|
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"label\t72\t-\tWeight\t19.687934144875545\n",
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"label\t89\t-\tWeight\t19.830805124280474\n",
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|
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"label\t90\t-\tWeight\t20.021426354120276\n",
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"label\t77\t-\tWeight\t20.05776182294002\n",
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"label\t34\t-\tWeight\t20.058245159709028\n",
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|
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"label\t70\t-\tWeight\t20.117786048649382\n",
|
|
||||||
"label\t68\t-\tWeight\t20.139598145778074\n",
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|
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"label\t88\t-\tWeight\t20.185751240083068\n",
|
|
||||||
"label\t38\t-\tWeight\t20.208902223231863\n",
|
|
||||||
"Latent semantic no. 8\n",
|
|
||||||
"label\t0\t-\tWeight\tnan\n",
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|
||||||
"label\t1\t-\tWeight\tnan\n",
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|
||||||
"label\t2\t-\tWeight\tnan\n",
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|
||||||
"label\t3\t-\tWeight\tnan\n",
|
|
||||||
"label\t4\t-\tWeight\tnan\n",
|
|
||||||
"label\t5\t-\tWeight\tnan\n",
|
|
||||||
"label\t6\t-\tWeight\tnan\n",
|
|
||||||
"label\t7\t-\tWeight\tnan\n",
|
|
||||||
"label\t8\t-\tWeight\tnan\n",
|
|
||||||
"label\t9\t-\tWeight\tnan\n",
|
|
||||||
"Latent semantic no. 9\n",
|
|
||||||
"label\t0\t-\tWeight\tnan\n",
|
|
||||||
"label\t1\t-\tWeight\tnan\n",
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|
||||||
"label\t2\t-\tWeight\tnan\n",
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|
||||||
"label\t3\t-\tWeight\tnan\n",
|
|
||||||
"label\t4\t-\tWeight\tnan\n",
|
|
||||||
"label\t5\t-\tWeight\tnan\n",
|
|
||||||
"label\t6\t-\tWeight\tnan\n",
|
|
||||||
"label\t7\t-\tWeight\tnan\n",
|
|
||||||
"label\t8\t-\tWeight\tnan\n",
|
|
||||||
"label\t9\t-\tWeight\tnan\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"ename": "TypeError",
|
|
||||||
"evalue": "Object of type ndarray is not JSON serializable",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task_5.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m find_label_label_similarity(fd_collection,selected_feature_model)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m extract_latent_semantics_from_sim_matrix(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m label_sim_matrix,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m selected_feature_model,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m k,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m )\n",
|
|
||||||
"File \u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\utils.py:1193\u001b[0m, in \u001b[0;36mextract_latent_semantics_from_sim_matrix\u001b[1;34m(sim_matrix, feature_model, sim_type, k, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 1187\u001b[0m \u001b[39mfor\u001b[39;00m label \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n\u001b[0;32m 1188\u001b[0m \u001b[39m# get representative vectors for the label\u001b[39;00m\n\u001b[0;32m 1189\u001b[0m label_mean_vectors\u001b[39m.\u001b[39mappend(\n\u001b[0;32m 1190\u001b[0m calculate_label_representatives(fd_collection, label, feature_model)\n\u001b[0;32m 1191\u001b[0m )\n\u001b[1;32m-> 1193\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((num_labels, num_labels))\n\u001b[0;32m 1195\u001b[0m \u001b[39m# Calculate half and fill the other\u001b[39;00m\n\u001b[0;32m 1196\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[0;32m 173\u001b[0m iterable \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m(skipkeys\u001b[39m=\u001b[39mskipkeys, ensure_ascii\u001b[39m=\u001b[39mensure_ascii,\n\u001b[0;32m 174\u001b[0m check_circular\u001b[39m=\u001b[39mcheck_circular, allow_nan\u001b[39m=\u001b[39mallow_nan, indent\u001b[39m=\u001b[39mindent,\n\u001b[0;32m 175\u001b[0m separators\u001b[39m=\u001b[39mseparators,\n\u001b[0;32m 176\u001b[0m default\u001b[39m=\u001b[39mdefault, sort_keys\u001b[39m=\u001b[39msort_keys, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\u001b[39m.\u001b[39miterencode(obj)\n\u001b[0;32m 177\u001b[0m \u001b[39m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[0;32m 178\u001b[0m \u001b[39m# a debuggability cost\u001b[39;00m\n\u001b[1;32m--> 179\u001b[0m \u001b[39mfor\u001b[39;49;00m chunk \u001b[39min\u001b[39;49;00m iterable:\n\u001b[0;32m 180\u001b[0m fp\u001b[39m.\u001b[39;49mwrite(chunk)\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:432\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[0;32m 431\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(o, \u001b[39mdict\u001b[39m):\n\u001b[1;32m--> 432\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n\u001b[0;32m 433\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 434\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:406\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_dict\u001b[1;34m(dct, _current_indent_level)\u001b[0m\n\u001b[0;32m 404\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 405\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 406\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 407\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 408\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:326\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_list\u001b[1;34m(lst, _current_indent_level)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 325\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 326\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 327\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 328\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:439\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 437\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mCircular reference detected\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 438\u001b[0m markers[markerid] \u001b[39m=\u001b[39m o\n\u001b[1;32m--> 439\u001b[0m o \u001b[39m=\u001b[39m _default(o)\n\u001b[0;32m 440\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[0;32m 441\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
|
|
||||||
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:180\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdefault\u001b[39m(\u001b[39mself\u001b[39m, o):\n\u001b[0;32m 162\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[0;32m 163\u001b[0m \u001b[39m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[0;32m 164\u001b[0m \u001b[39m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 178\u001b[0m \n\u001b[0;32m 179\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 180\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mObject of type \u001b[39m\u001b[39m{\u001b[39;00mo\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 181\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mis not JSON serializable\u001b[39m\u001b[39m'\u001b[39m)\n",
|
|
||||||
"\u001b[1;31mTypeError\u001b[0m: Object of type ndarray is not JSON serializable"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"selected_feature_model = valid_feature_models[\n",
|
"selected_feature_model = valid_feature_models[\n",
|
||||||
" str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
|
" str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
|
||||||
@ -200,7 +50,7 @@
|
|||||||
" k,\n",
|
" k,\n",
|
||||||
" selected_dim_reduction_method,\n",
|
" selected_dim_reduction_method,\n",
|
||||||
" top_images=10,\n",
|
" top_images=10,\n",
|
||||||
")\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:
|
||||||
@ -1054,7 +1058,7 @@ 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)),
|
||||||
@ -1089,7 +1093,7 @@ 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)),
|
||||||
@ -1122,7 +1126,7 @@ 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)),
|
||||||
@ -1140,10 +1144,10 @@ 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)),
|
||||||
@ -1152,9 +1156,8 @@ def extract_latent_semantics_from_sim_matrix(
|
|||||||
)[:top_images]
|
)[:top_images]
|
||||||
for latent_semantic in Y.T
|
for latent_semantic in Y.T
|
||||||
]
|
]
|
||||||
|
|
||||||
if valid_dim_reduction_methods[dim_reduction_method] == 4:
|
|
||||||
print("Note: for K-Means we display distances, in ascending order")
|
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:
|
||||||
@ -1185,19 +1188,17 @@ 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
|
||||||
@ -1219,14 +1220,30 @@ 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…
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Reference in New Issue
Block a user