still fixign gitignore

.gitignore

@@ -9,7 +9,7 @@ code/simclr-pytorch-reefs/evaluation/model_states_i2map_simclr
 scratch/baseline
 code/simclr-pytorch-reefs/evaluation/embeddings/raw_embeddings/
 code/simclr-pytorch-reefs/evaluation/embeddings/raw_embeddings
-code/simclr-pytorch-reefs/evaluation/embeddings/log_metrics
+code/simclr-pytorch-reefs/evaluation/log_metrics
 
 
 

code/simclr-pytorch-reefs/evaluation/embeddings/YAMNet_embedding_extractor.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import torch\n",
+    "# import torch.nn as nn\n",
+    "# import torchvision.models as models\n",
+    "# from torch.utils.data import DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing necessary modules\n",
+    "import json\n",
+    "\n",
+    "# Load the JSON file\n",
+    "json_path = '/home/ben/reef-audio-representation-learning/data/dataset.json'\n",
+    "with open(json_path, 'r') as f:\n",
+    "    dataset_json = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize an empty list to store the filtered entries\n",
+    "filtered_entries = []\n",
+    "\n",
+    "# Filter entries based on 'data_type' and 'dataset'\n",
+    "for entry in dataset_json['audio']:\n",
+    "    if entry['data_type'] == 'test_data' and entry['dataset'] == 'test_australia':\n",
+    "        # Convert the 'class' to numeric\n",
+    "        numeric_class = int(entry['class'].replace('class', ''))\n",
+    "        \n",
+    "        # Create a new dictionary with 'file_path' and numeric 'class'\n",
+    "        filtered_entry = {\n",
+    "            'file_name': entry['file_name'],\n",
+    "            'class': numeric_class\n",
+    "        }\n",
+    "        \n",
+    "        # Append the filtered entry to the list\n",
+    "        filtered_entries.append(filtered_entry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "simclr_pytorch_reefs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}