{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Visualise embeddings of the JDE model\n", "\n", "This notebook embeds images from the training data using the JDE model. It then collects all embeddings and projects them using different techniques (e.g. UMAP, PCA). These projections are plotted; try to hover the plot to see the source detection.\n", "\n", "In a second step these images are drawn onto a canvas using the projected points." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import glob\n", "import pickle\n", "from typing import TypedDict\n", "from tqdm.auto import tqdm\n", "\n", "import os\n", "import numpy as np\n", "\n", "import logging\n", "import argparse\n", "logger = logging.getLogger(__name__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-04-05 13:28:53 [WARNING]: Matplotlib created a temporary config/cache directory at /tmp/matplotlib-zuapevtb because the default path (/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n", "2023-04-05 13:28:53 [INFO]: generated new fontManager\n" ] } ], "source": [ "from track import eval_seq\n", "\n", "from utils.parse_config import parse_model_cfg\n", "from utils.utils import mkdir_if_missing\n", "import utils.datasets as datasets\n", "from utils.log import logger as trmlog # we need to override this...\n", "\n", "\n", "trmlog.setLevel(logging.INFO)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import umap # should provide better results than t-SNE\n", "\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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